So it's quarter past one and I welcome you to the afternoon session of the first day of our summer school. And it's my pleasure to introduce the first speaker of the afternoon, Chloé Agard Asen-Cott, an expert at the interface of machine learning and bioinformatics, Chloé Dieter, PhD in computer science at UC Irvine, then was a postdoctoral research scientist at the Max Planck Institute in Tübingen, and then moved on to Paris to mean Paris Tech, the Institute Curie and Inserm, where she, in 2018, became an assistant professor and is now an associate professor. Chloé has worked a lot at this interface between machine learning and computational biology. And she has received a number of honors for her work. Like she became an Alexander von Humboldt research fellow during her postdoctoral studies. And she is now even co-president of the Community of Special Interest for Machine Learning in Computational and Systems Biology, one of the big cosies within the International Society of Computational Biology. She's also very active in the Society of Women in Machine Learning and Data Science, where she co-founded the Paris section of this society. And she received an A&R Young Research Grant in 2019. I'm very happy to have her here. I was also very happy to have her in my lab during her postdoctoral studies. And when I read the title of Chloé's talk, I remember the conversation we had 10 years ago about multiple modalities and how we can do machine learning when we have multiple views on the same data. I must admit, 10 years back, this was a very hypothetical discussion because we hardly ever had more than one view. But now, 10 years later, this is absolutely a reality. It also tells you a bit about how you can develop algorithms now that might become useful in a decade from now, which is maybe like an aspect of our field. So I'm very interested to hear your current perspective on this topic, Chloé. And we are very happy to have you here. So thank you, Carson. Thank you for the introduction. It's a pleasure to be here. It's a pleasure to be actually talking at one of this machine learning and precision medicine summer school because I was one of the organizers of the first such summer school we had in 2014. And now, finally, I'm a speaker. So my goal here is, so I have one of those lectures, of course, not invited talks. And so my goal is to give you an overview of some machine learning techniques for data integration, giving you an overview of everything that would be completely out. I mean, it's impossible. There's so many things happening. So yeah, actually, bouncing back on Carson's remark in his introduction, some of the things I'm going to present are actually a decade old or more because what I want to do here is not so much talk about the very latest research in this area, but maybe lay a bit the foundations of some major ideas that are still very relevant today. OK, so the first thing maybe is to clarify what we mean by, so my title had data integration. So what do we mean by data integration or data fusion in a machine learning perspective? The idea here is that you have multiple views of the data. So often nowadays, we talk about multiple modalities. Sometimes you'll hear in the bioinformatics from Mathiomics, the idea is that you have several representations of the same data set. So you have your learning data set is composed of n samples. And then instead of having just one set of n vectors representing your n samples, you have several such sets. So here I'm representing two of those. So they don't have to be coming from the same space. And so the idea is that you want to learn from both's representations at the same time. So that's what used to be called multi-view machine learning. And the deep learning community seems to be insisting on multimodal machine learning. So I'll tend to use both interchangeably. And the assumption here is that those views are complementary. So they're both bringing information and that there's a benefit to be gained from learning from these two representations at the same time, that you're not encoding the same information in two different views. OK, so first a few examples of multi-view learning problems. I've actually only picked two examples, but so I could have given an entire lecture on that. The first one comes from cancer research and multi-omics data. So this is, I mean, I've picked one paper. You see these references at the end. I've picked one such paper, but there are many others based on TCGA data and other cancer data. Where the question is, can you combine data coming from sequencing, so whether exam sequencing, gene expression data, this type of things. Data from SNPs, sourcing on the nucleotide polymorphisms, methylation data, protein levels. So can you combine all those different omics views of the same samples to identify disease subtypes? So in machine learning terms, this means clustering. And identify which types of, I mean, among all your samples, which are the samples that shared common characteristics. Another example I've picked is much more recent paper, a multimodal prognosis prediction also in cancer. What I liked about this example is that this idea of combining omics data, clinical data, and imaging data. So forget about the details here. I just wanted to put a picture. So I picked it from the paper. But you'll find a bunch of work on multi-omics, so as on this previous slide. But there's also now one more work where you incorporate clinical data and full-site imaging histopathology data, which I think is very interesting because these are very different, I mean, the nature of these different objects is very different. OK, so those were my two examples to help you figure out what it is I'm talking about with this multi-view machine learning. And so now a bit of classification of multi-view machine learning techniques based on the stage of integration. So I'm not the person who came up with this. It's been around for a while. The idea is that you can do integration of multiple views at different stages. So the first stage is early integration. And the idea of early integration is that you're merely going to concatenate the different features to obtain a classical problem, single-view problem. So if you have two views, one with P1 features and the second with P2 features, then you're just like concatenating your vectors, your input vectors. And now you have a single view of P1 plus P2 features. So the nice thing is that it's really easy to do. I mean, if you know, if you have software that runs machine learning algorithms, you just like inputs the concatenation of your different views. And here you are. The limitations are numerous. One is that you're going to think hard about how you're normalizing your data if you have measurements that are on different scales. What does it mean to give as input to your algorithm numbers that for some of them are doing expression levels and for others are presence or absence of mesolation? So it is a bit just combining apples and oranges, which makes both learning and interpretability difficult. And another limitation is that, I mean, especially if you're talking multi-omics, each of your view is already typically a very high-dimensional vector. So you have the next question for, I don't know, 20,000 transcripts, SNPs for SNP data, presence, absence for a million SNPs, mesolation data for, I don't know, how many CPD islands. So if you're just using one of those early integration things, you're making your cursive dimensionality issue even stronger than what we already have in single-view omics data. OK, so the second family of approaches would be light integration, where you would learn different models on each of your views. And then you would combine all, you would combine the outputs of those views. So that's maybe not very clear in my notation, but so here I have two views. So I chose vectors of P1 and P2. So I'm learning one model on the first view, a second model on the second view. And now I have a third function, a third model G that is combining what I've learned with F1 with what I've learned with F2. And you could either use a preset function for G. So if you're doing a regression problem, you could average your outputs. If you're doing classification, you can use majority vote. But of course, very often what you're trying to do if you're employing one of those approaches is to learn this function. So you're going to learn first all your models and then learn how to combine them. So this has the advantage. I mean, this addresses some of the limitations I've listed before for the early integration. So namely, because you're dealing with each of your views separately to start with, you don't have these problems that I was calling mixing outputs and oranges. And also, your cursive dimensionality is limited to cursive dimensionality on each of your views instead of on the sum of all your views. And again, it's fairly easy to set up if you have a classical machine learning library. One of the limitations of late integration is that if you want to do something a bit smart here in G, you're going back to what would be ensemble learning. So you can learn different models. Then you want to combine them in a smart way. And we know that ensemble learning works better if models are uncorrelated. And here, of course, I've said the assumption was that your different views were complementary and not encoding the same information. But at the same time, you would expect that they are correlated. And this makes it difficult to benefit from more than one view. And my limited experience with this type of problems, with this type of approaches, is that if you have one of your models that is performing really well, it's difficult to add more information and make the whole thing perform better. OK, so now I'm coming to what I find as the most interesting type of approaches, which is what I'm going to be talking about for the reminder of my talk, which is intermediate integration. So here's the idea is that really you're going to jointly learn from the two views at the same time with a specific, I mean, a dedicated algorithm that is specific to multi-view problems. So I'm really going to learn in one go a function from the, I mean, across the different views, on the over the union of the different views. OK, so from a modeling point of view, it's much more satisfying. So you're explicitly modeling the fact that you have multiple data sources. And you can do, I mean, then we'll talk about this. If there's relationships between your different sources, you can model them. But from a practical point of view, this requires coming up with new algorithms. And so this makes it more difficult. OK, so that's what we're going to be talking about today. All right, so there's several ideas on how to do intermediate data integration, machine learning, multi-view machine learning that I want to talk about. And the first idea, several families of ideas. The first one is the idea of embedding the data in a common feature space. So the idea is that you want to take your different views of the data. You want to map them to a single space in which you have a common representation of the data, and then apply a classical machine learning algorithm to this common representation of the data, which is expected to be something more meaningful than just a concatenation of the two views. OK, so when I'm talking about embedding the data in a common feature space, which may be the first thing that comes to mind, is that you want to find a low-dimensional representation of the data that comes from all your different views. So I think depending on the slides, I'm only writing two views or I'm writing a capital V views. But most of what I'm talking about can be generalized if I'm writing it with two views. So most of what I'm talking about can be generalized to an arbitrary number of views. OK, so what I want to do here is learn a representation of my data. So I have a certain number of features in each of my input spaces in each of my views. And I want to learn a representation of my data in a space whose dimensionality is much smaller than the sum of P1 and P2. And the first example of this is joint non-negative matrix vectorization. So you might know already non-negative matrix vectorization, it's a classical technique for dimensionality reduction in machine learning. So here at the bottom of my slide, I'm presenting NMF, so non-negative matrix vectorization, for a single view. So here I have a data set. So we're talking here about unsupervised machine learning. So I have a single view and samples, P dimensions. The idea of NMF is that you're going to find a new representation of the matrix. Here it's this new representation is called W. You still have N rows, but you have only D dimensions and D is much smaller than P. So this is this matrix here. And you're going to find it by decomposing the data matrix and the product of such a matrix W and a matrix H that gives you the correspondence between each of this D dimension and the P dimensions that you originally had. So it's called non-negative because you're imposing the constraint that all the entries in W and H are positive. And so formulation of this problem is to find, so if D is set, find W and H so as to minimize the forbidden norm of the difference between X and W, H. So what this means is merely that you're going to take the sum over the squares of the differences element-wise between you two matrices. So if you want W, U, H to be as close as possible to X element per element. So this is a big family of approaches. And NMF has been widely studied for several algorithms that exist that are fairly efficient. There's several variants which you can add constraints to these problems. And so it comes in many flavors. And so one of this flavor is a multi-view approach where you want to learn a view to still W, a representation W of your data, D dimensional, but such that you have an approximation so that W approximates, sorry, that W H approximates each of your views. So that's what I have here. So you could do non-negative matrix factorization on each of your views independently. And then you would have, if you have V views, you would have V representations. But what you're doing here that you enforce that the representation in the low-dimensional representation is the same for all the views. So I'm not going to talk more about this because I think Anais Boudou may talk a bit about it tomorrow. And if you're curious about examples of application of this technique to multi-omics data, you can check out this review paper here by Laura Contini and Anais Boudou. And so that was my first approach to learn a lower-dimensional representation of data from several different views. So this is the old school type of thing to do, matrix factorization. Nowadays, what you do when you're cool is deep learning. And so you probably know deep learning. You can always see it as learning of representation of the data. So you put whatever number of hidden layer between your input and your output. And the last hidden layer, you can always interpret it as a new representation of your data. And so there's a bunch of approaches that have been developed for deep multi-view learning where, in essence, you're going to put your views, your different views as inputs to the neural network. But instead, I mean, unlike what you would do with, I mean, if you were just doing early integration, you will start by, you will, for the first layer or the first few layers, you would connect the views to their own hidden layers and not connect the entire input layer with the entire next hidden layer. So here at this stage, you learn an intermediate representation of each of your views. And here in blue, you have a global intermediate representation. So again, on my drawing, I have only have here one hidden layer specific to each view and one hidden layer that is common to both views. You could imagine having many more intermediate layers. And you could do that in either a supervised or unsupervised ways with feed. So supervised would be just a feed forward architecture with, so you would, and unsupervised would be an autoencoder. So when you would want the output to match the input. And the idea is that because here you have a single representation at the end, I mean, because, sorry, the idea is that this architecture will start by transforming if you want each of your views so as to make them as amenable as possible to be transformed into a last representation common to both views. And so typically when you do this, you also use your intermediate layers would be a smaller dimension than your input layers, which is why it's inspired similar to the NMF. It's also a dimensional iterative action technique. OK. So that was the first point of my first idea, embedding the data in a common feature space. On the other hand, you might want to, if you're working, if you like kernels, you might want to map the data to a higher dimensional, I mean, typically the hell of a space that's higher dimensional than your input data. And that is, again, common to that in which a common representation to different views lives.  I'm talking about kernel methods. So what I'm talking about here is actually about learning a kernel, which is going to work on pairs of elements represented by my different views. And I thought I would insert here a little primer on kernel methods. I don't know if everybody who's listening today is very familiar with kernels. So the idea of kernel methods is to build nonlinear models by transforming your data by mapping into a space, new space that is typically higher dimensional. And in this new space, you're going to learn a linear function that's going to be nonlinear on your input space. So I have here a small example, which I like, because I don't have to draw a higher dimensional space, which is difficult if I only have two dimensions on my slides. So here, I have two features. And I have a model. So you can imagine that it's a decision boundary separating two classes. This model is nonlinear. But this nonlinear model is actually linear in a different feature space. That's over x1 square and x2 square. So kernels allow you to generalize this in the sense that so you can use a mapping much more complicated than what I've used here to go from your input space that has p dimension to a new space that has many more dimensions. And a dot product in the new space is simply a kernel over the initial space. So my original input data, a kernel over the original inputs, just means dot product between the images of x and x prime in the new space. And why do people like working with kernels if they use something called a kernel trick? So it tells you that if you have an algorithm in which your input x appear in dot products, then you can replace this dot products by a kernel. It's equivalent to mapping the data to a new feature space through this function phi and replacing the dot products between the images of the point x and x prime through phi with a kernel.  It doesn't sound like a trick at all. It means I've replaced writing this with this. And it's so far, I mean, dot product between phi of x, phi of x prime is the same thing as k of x and x prime. Doesn't look so much like a trick, but it actually is a trick when computing k is easier than computing phi. And you have cases in which actually you can find a k in situations where you don't have an explicit phi. And even if you're not in such a situation, on the example here with the quadratic kernel, so the quadratic mapping was simply mapping your P features to themselves plus the pairwise products between all features, so from x1 square, x1, x2, x1, x3, and so on and so forth, until you reach x space squared. So it's quite a large number of features. With some, I mean, you have some coefficients to take into account if you want these two expressions to match, but taking the images of x and x prime through this function and then applying the dot products is equivalent to taking the dot products in the initial space, adding a constant, and putting everything square. So this is what we call the quadratic kernel. And this is a situation where you can imagine that it's easier to keep my P features, compute the dot product at a constant, multiply by itself, versus then first computing all my features. So it's not very interesting for quadratic kernels, but it becomes interesting for you can imagine, for instance, a higher degree polynomial. Kernels are also interesting in computational biology, because there's been a bunch of kernels that have been developed for biological data. And so I'm going just to mention a few examples. I've put pointers to this if you're interested. So you have kernels based actually not on vector or representation of the data, but just on sequences. And you can use such kernels to build kernels directly over sequences, and in particular, coding sequences. You can build kernels based on networks. So whether graph kernels for objects that themselves can be represented as graphs, so graph kernels for molecules, or if you have a network on which your nodes are the objects that you want to build a kernel on, you have things like diffusion kernels that can also give you a kernel directly without first finding a mapping. We also have kernels for SNPs in genome-wide association studies. And all those things are possible because kernels can be interpreted as measures of similarities. So in order to build a kernel, what you need to do is to find some notion of similarity. What does it mean for two objects to be similar? So what does it mean, for instance, for two protein sequences to be similar? And then you just need just, sorry, just as sometimes difficult. But if you have something that, in terms of meaning, can be interpreted as a similarity, and in addition, verifies a number of mathematical properties, and you can use it as a kernel. And the reason why a kernel can be interpreted as a measure of similarities is because it's a dot product. And if you think of dot products in Euclidean space, the dot product between those two vector x and x prime is proportional to the cosine of the angle. So if the vector are collinear, the dot product is large. The cosine of the angle is 1. And those two vectors are orthogonal. The dot is there very different. The cosine of the angle is 0. OK, so kernels have a long history of being used in bioinformatics. So I think it makes sense to wonder if you're interested in integrating data in multiomics context to wonder about the kernel approaches that can be used. And so the idea of here is going to be the idea of multiple kernel learning. So you have your different omics data, and you know from the literature how to build one kernel for each of your data types. So each of those k's here is actually a matrix of n times size n by n. So n is, again, your number of samples. And each entry of this matrix is the kernel function applied to sample i and sample l. So it's a dot product between sample i and sample l, but in this high dimensional space. So a sum of kernel is a kernel. So you can build a multi-view kernel that is the sum of linear combinations of those different kernels. And you know that if because you know it's a kernel, you know that there exists a heap of space and a mapping from your input data to this heap of space such that this kernel is a dot product over this heap of space, which means that you can use any kernel algorithms, kernel-based with this linear combination of kernels. So now I haven't seen much about this coefficient here and the linear combination. So one of the things you can do is normalize your kernel matrices so that they all have one on the diagonals and just take the sum. But actually there's something interesting in the case of support vector machines is that you can also learn ideal optimal values for this coefficients. So here I've written the dual formulation of support vector machines. So if you're not very familiar with SVMs, so this is one of the formulations, one of the ways of formulating the optimization problems that when you solve it gives you an SVM decision function. So what it's doing is that it's learning a function that is for, so the decision function is going to be a linear combination of the product between the labels of the samples from the dataset and the kernels between a new sample, the one you're trying to label and samples from the dataset. And so, and one thing that's also important to note is that we know how to solve this problem efficiently and exactly. So one thing we also know from the theory of SVMs is that, so here what you're trying to do is find those coefficient alpha of the linear combination. But we also know that you have the better performance when this function here that you're trying to maximize is smaller. So I'm solving this problem. I have a certain performance. If I have other data points and I'm solving this problem and I'm obtaining model that has a smaller minimum, then my second model is going to be performing batches in the first one. Which means that we know how to optimize the kernel while learning by looking. So here, all what I've done is that I've replaced this kernel here by my multiple kernel, my linear combination of the kernels of the different views and I can replace. And I know that I want this whole thing to be minimal. So I'm going to look for the values here of the coefficients mu of the linear combinations that minimize the overall optimum. So this is ideas that have been proposed quite a while ago now. Almost 20 years ago. And so maybe to summarize what we're doing here is that we start from a kernel on each of our different views. And then we can learn a linear combination of those kernels that is optimal at the same time as we're learning an SVM that uses this kernel to make its decision. Okay, so in the two, I mean, I've presented you so far approaches for intermediate integration in multi-view machine learning, whereas the idea was to map all the different views onto a common subspace and then learn a function on the subspace. A second family of ideas would be to learn different models on each views, but at the same time, forth them too much. So what I'm talking about here is that if I have my two views, so one on a space of dimension P1 and a second on a space of dimension P2, I'm going to learn two models, F1 and F2. But unlike what I was doing in the late integration, now I want to learn them. And at the same time, as I learned them, I want to enforce that those two models make, they agree that they make similar predictions. And then in the end, I can build a final model that is a simple combinations. So average or weighted average majority vote of those different view specific models. One of the nice advantages of this type of integration is that in the end, because you obtain one model per view, you're able to make predictions if you're in a certified setting or cluster points, integrate points, if you're in a clustering application, for instance, even if one of your views is missing. So those approaches are particularly suited to problems where you might not have all your different types of data available to you at prediction time, which I think makes a lot of sense in multi-omics settings. You might have a bunch of different type of information that's learning stage or like gene expression, mesolation data, mutation data, and so on and so forth. But you want to be able to make predictions even if one of those modalities is missing whether because no one ever acquired it or because there's some quality control issue on it. So I think those approaches are particularly interesting for this reason. And here, I want to talk about, again, two major ideas. One is CCA, and the other is to impose these agreements through regularization. Okay, so CCA is really old technique, hard-telling, 1936, it's almost 100 years old. The idea is that you want to find basis vectors, so vectors of new space on which you're going to project your data, so as to maximize the correlation between the projections of two views. So this is, here's the problem you want to solve. So I want that I project view one over vector one, if it's W1, and I project view two over vector W2, then the dot product here between those two projections is maximum, and I can impose some constraint that just to have a unique solution to the problem. So it's a bit similar in spirit to PCA, where you want to find directions on which to project your data, so as to maximize its variance, but here you have two views, and you want to project each of them on, I have a new representation for each of them in such a way that they're maximally correlated in the new space. Another way to look at it, I mean, there's two formulations, the equivalent is that you want to minimize the disagreement between the projections. So here is again, I'm projecting data one, data X1, my view, my first view, sorry, on W1. I'm projecting my second view on W2, and I want the difference between those two to be minimal. So this naturally extends to more than two views. And there's many extensions of CCAs, I mean, because it's been around for so long, including using kernels, there's those multi-view extensions, you have catering in 71, you have bunch of work on CCA. So here again, with CCA, we're talking about dimensionality reduction, and each of your view is going to be sent to different representations, so keep that in mind. It's just that those different representations are going to agree among themselves. Okay, so it's not exactly this idea of, I mean, it's exactly this idea of learning different models on different views and making them agree among themselves, but in a supervised setup, you're still missing one step. How do I go from having those different representations that are still one representation per view to having a single unified model on top of the views? And the answers for that is typically through regularization. So here's the idea is, I mean, I hope you're familiar with the idea of regularization, just to explain it in a few words. This is, so here I'm in a supervised learning setup, and supervised learning is typically empirical risk minimization, which means that I define myself a family of models, so capital F, and I'm looking for the model in this family that minimizes the empirical risk, and the empirical risk is simply the average error that my model is performing on this data set. So here, L is the loss function, so L of YI, FV of XI is the errors that I'm making by predicting F of X instead of Y, so when the true label is Y. And so all the supervised machine learning algorithms you know are almost all following this setup, provided you add a regularization term, sorry, I don't know why it says loss risk here, but it should be regularization. A typical regularization function is the Elton norm of the weight vector of a regression coefficient, for instance. So regression enters this framework, SVMs enters this framework, and so that's when you can do view per view. And so regularizers can also be used to tie the different views together. So here you have in black, you can see the exact same thing as before, so for each of your different views. So if I was only solving, finding the minimum over the sum for this different views of my empirical risk with a view-specific regularizer, it would be equivalent to finding V different models and learning them independently from each other. But I can add an additional regularizer over the consensus, that's going to encourage my V different solutions to be similar to each other. And so there's many examples of that. One of them is on the SVM. So an approach from 2005 to 2006, that's called SVM2K. So if I go back to the primal formulation of the SVM, this is the problem you're trying to solve. So you're minimizing, you're looking for a weight vector. So I'm not using kernels here. Here you're looking for a weight vector W that is such that you minimize the sum of regularization term to norm plus the sum of slight variables or slight variables or the errors that you're making on each of your samples. So the prediction function is Wx plus B. The true label is Y. And the slack tells you how far you are from having Y times Wx plus B greater than one. And what SVM2K is doing is repeating this problem. So here you have it for the first view. If I'm solving, if I'm minimizing this, I'm finding an SVM over my first view. If I'm minimizing this, I'm finding an SVM over my second view. And what we're doing with SVM2K is add a constraint here that is the consensus. So those eta i are small when the two views agree in their prediction. And so here you have a number of constraints that are again those constraints here for the first view and for the second view. And in orange, I've highlighted the constraints that tells you that the two views should agree. And then in the end, your prediction, your final prediction is just the average of the two predictions. And of course this extends to more than two views. So that's the first example. You can also do that with lasso type of approaches. So lasso is an empirical risk minimization on the linear model again. So here I wrote it for quadratic, sorry, for regression problem. So I'm using the quadratic error. I have an L1 norm here regularizing my regression coefficients. And one way you can tie together several lassoes on different views is what I've written here. So here in black, you have the same thing as before. So learning on your view views separately. And then you're using what's in orange to tie your views together. So you're using this matrix MIV. So MIV is a contribution of view V to label I. So to the label of sample I in your training set. And you want this whole matrix M to be sparse and no rank. And this is what's going to tie your different models together. And in the end, so you've learned that your final model it's a linear combination of the models you've learned on each view. And the coefficient for this linear model, it depends. I mean, it's sort of an average of those coefficients, those contributions of the different uses, different labels. You could imagine different regularizers that would tie your views together. Actually, I mean, I had one in mind and then I looked for a reference for it in the literature and I couldn't find it. So I don't know if I'm the only person thought about it. I don't probably unlikely. Maybe I wasn't good at searching the literature or maybe it actually doesn't work in practice. But that's one of the ways you can tie together several lessons. And you can apply this approach to NMF as well. So in the, so I had presented you NMF for a single view earlier on. And then I told you if you do joint NMF by looking for a same matrix W that would be common to your different NMFs for each of the different views. But what you can also do is allow yourself to have a different projections, a different WV for each of the views. But then you want to use a regularizer to tie together those different matrices here. They're not too different from each other. So again, a regularization approach. Okay, all right. So I've already talked about two of my favorite things in machine learning, which are kernels and regularization. The one thing I haven't talked about so far is graphs. And so that's what I want to be talking about now is how people have been using graphs to do multi-view machine learning. Okay, so first a small recap on graphs. So you can use graphs to model relationships between entities. So graph is a set of vertices and edges. And the edges are pairs of vertices. So here you have a graphical representation of the graph. So it has nine vertices and a number of edges I haven't counted. And you can also represent a graph by a matrix. So it's called a adjacency matrix. I mean, by several types of matrices actually, but here is the adjacency matrix. This is one of the most common ways of doing that. So you have as many lines, so it's square matrix. You have as many lines and columns as you have nodes of vertices in your graph. And each entry of this matrix is non-zero if and only if and only if there is an edge between the two vertices. So here there's an edge between the first and the third vertexes. So there is a one at position one, three in my matrix. There's a bunch of variants on this idea. One of them is that instead of having ones and zeros, you can have weights over the edges. So here you would write weights, so labels on the edges. And inside the adjacency matrix, just being a binary matrix, you would have real value numbers. And you can also orient the edges. So you would have arrows on the edges and then the adjacency matrix wouldn't be symmetric and symmetric anymore. So having an edge from V1 to V3 wouldn't be the same as having an edge from V3 to V1. And graphs has been used a lot, in particular in bioinformatics to represent two things. First knowledge, prior knowledge that you have about relationships between entities. So maybe one of the most classical examples in biology is biological pathways. So your nodes would be edges. The relationships tell you that those edges tell you that those genes belong to the same pathway or work together towards achieving a biological function. And you can also build graphs from any data. You can always build a graph as a matrix of similarity. So your adjacency matrix would be a similarity matrix. You could threshold it and say, if you wanted binary, so you compute similarities. I don't know, you use dot product and you leave in space, for instance, on your input data. And then you would have, okay, those feature one and feature three, they're similar. The similarity is above a certain threshold. So I put one here, I put an edge between them. If you feature one and feature two, there aren't, so I don't put any edge between them. Okay, so graphs, graphs can be used to represent a relationship between samples. So you would have each of two different views, gives you a different adjacency matrix which is so N by N matrix. So each of you have used a few different graphs, but with the same nodes. And then supervised learning can be seen as a note labeling problem. So if I have some of the labels for some of this edge, for some of those nodes, how do I learn the labels for the missing nodes? Not going to talk about this because I think Anais will talk about it tomorrow. What I want to talk about is the idea of using graphs to tie views together in the context when features from each view can be mapped to a single vertex in the graph. So let me clarify what I think about that. So when you're working with omics data in particular, you can very often match your different omics features to genes. So if your features are RNA transcripts on the one side and protein levels on the other side, of course, you know how to map those to the same gene. You can also do this with SNPs. And I'm mentioning this here. This is something we've been exploring because it's ML, FPM work. It's something we've been exploring in the context of GWIS with Christelle and Jen. So Christelle, Benz, Tina, and Jen, Jero. So how do maps, SNPs to genes and the main idea is that you map SNPs to gene based on positions on the DNA sequence. But also you can map SNPs to genes based on known regulatory information. So you know that this map regulates the expression of this gene, so you map it. And also proximity, not on the sequence, but in 3D space, which should also include some regulatory informations. So then what this means is that you have your graph, which comes, for instance, from prior knowledge, like biological networks, let's say protein-protein interaction networks. And then so each, this is a graph of your features. Each node, each vertex is a feature. And then each of the views is a label of this node. So you can have one, you can see this, as you have one graph per view, same graph structure for the different views, but different feature values for each view and for each sample. And so that what I've tried to represent here for one view is that I have a graph structure here that is common to everyone, but sample I has a different label for each node, for example, I mean, sample I here has a different label for this node, which is the second gene, as a different sample, so sample L would have. And now there's several things you can do with such a representation. One would be to say, okay, so I have now one, each of my samples is represented by a graph or several graphs. One per view. And I know how to build kernels on graphs. So I can use graph kernels to build one kernel per view and apply multiple kernel approaches. And so one successful approach for this is called PAMOC. It's a pathway, multiomics graph kernel based approach. And it does, I mean, I've summarized it in one sentence, but this is what it does. So each of your sample is a different set of labels over this graph. You use graph kernels that don't care so much about the structure of the graph, but care about the labels of the nodes. And then you can use those to build kernels over each of the views of your data. I think Crystal will also talk about some examples in this field tomorrow. So what I want to talk about now is how to use graph regularization to guide feature selection with such approaches. So I'm cheating a bit here because I'm actually not in a multi-view framework anymore. I mean, we could discuss how to extend those idea to multi-views learning, but to the best of my knowledge, it hasn't been done yet. And so I'm going to cheat and say that you have a graph that is built from one or several of your views and that you want to use this to select features based on another of those views. So there's several, so if I come back to my picture here, now the thing is that I want to use this data representation to learn a model in such a, and I want to learn which of those nodes are important for my output. So there's several type of families of approaches you can take, but they're all based on regularizations. I mean, also they want to talk about. And so there's several proposals for this that have been built on the lasso. And so you want to use the lasso to encourage sparsities. This is the future selection part. And then you want to use a graph to build the regularizers that encourages connected features to have similar weight. And so this is again your classical lasso formulation. Here's this is regularization parameter for the sparsity. And now you're adding this additional regularizer. So it's two examples of this regularizer as a generalized fuse lasso proposed by Tip Shirani and others in 2005, where here this regularizer is a constraint over the absolute difference between the regression coefficients on regression coefficients that correspond to features that are connected by an edge. So what this using this regularizer does is encouraging that two features that are connected on your network, they get similar weights. The network constraint lasso also does that in a slightly different ways. It uses a Laplace in regularization. I'm not going to get into the details for today, but it's a very similar idea. It's going to use square difference between the square of the difference between the coefficients and the absolute difference. In that set up, you also have other type of approaches where you're really focusing on selection. And when you start by computing the relevance for each feature, so if I go back to my old drawing here, I've used my end samples to compute for each of those nodes a relevance. So this can be correlation between the feature and the outcome can be based on the statistical test in the nonlinear measure of independence like H6. And now I'll use the graph to select few features with higher reverence that are connected on the graph. So these are approaches that have been explored in particular in the previous iteration of this ITN. So I thought it was historically relevant as well. And the formulation is very similar to that of the lasso instead of minimizing an error term, the risk you're maximizing a relevance under some constraints. And so one of the constraints is on the size of the number of features you're selecting. So you want to make sure that the size of the set S which is your set of selected features is small. And then you have a constraint on the graph. And so we have two variants of that, the one we proposed with Carson and others in 2013. Whereas this graph constraint looks like the graph Laplacian constraint of the network connected lasso. Whereas this constraint, you're going to penalize having disconnected solutions. So what I mean by that is that every time you have an edge between the features that is selected and a feature that is not selected, this is going to decrease your, the time you want to maximize. And then a few years later in collaboration with the lab of Provence de Menet, the lab of Carson proposed SIGMOD, which has a slightly different penalty, which instead of penalizing disconnected solutions and courageous connected solutions. Okay, I'm almost done with my overviews of this as ideas, but I think it's very interesting to think about using prior knowledge to tie together different views in omics data. And so I've shown you how this can be done with graphs, but this can also be done. I mean, you're going to tell me that it's a form of graph as well. You can also do this in deep learning by building knowledge and form your network architectures. So this is something that is sometimes called visible ML, visible machine learning. I don't like it so much because it really applies to deep learning. So I draw the call that knowledge and form deep learning architectures. The idea here is that instead of building a classical feed forward fully connected architecture over your neural network, you're going to connect different layers. I mean, you're going to give biological meaning to each of your internal jet levels, each of your hidden layers. So for instance, if your input features are genes, then you can have the next hidden layers. You're only going to connect to the next hidden layers to you're going to connect together genes that belong to the same complexes. And then you're only going to build connections between different complexes that belong to the same pathways. And so this is one example, there's many variations on this idea. The idea here is really that instead of having something fully connected with like no, I mean, just out of the box architecture, you only make connections between things that make sense to you. Between features that for you, it makes sense to connect based on prior knowledge. And this allows you to reduce your hypothesis space or reduce the number of possibilities for models that you're learning. It's tying together things that features that have meaning together. And I think it's also one of the ways to address this cursor dimensionality that is playing us in bioinformatics. I'm also mentioning that Peline, who is one of the fellows of the current ITN, working with Joaquin Dupazzo, as far as I know has been working on similar ideas during her PhD. All right, so I'm almost at the end of what I wanted to show you today. And the last thing I want to talk about is feature selection and interpretability. So a few of the messages I've talked about were focused on feature selection, but one of the limitations of all those approaches, I mean, of several of the approach that I've presented is that it's difficult to get anything explainable, interpretable out of it and to know which are the relevance features from your input, I mean, from your inputs or from your data, what is really driving your algorithm. And we have many opportunities in bioinformatics to wish for interpretable models. And so that's what I've tried to represent here is that we have two views and there are many features in each of the views, but I'd like to find out in each of the views which are the important features. So actually just jot it down a few ideas here. Of course, the first thing you can do is that if you're doing all the old age integration, you can apply a single view feature selection techniques. So anything you can think of from using sparse from learning sparse models to using feature importance random forest using sharp or lime and so on and so forth using attention, if you're using deep learning, all of this applies to your early or late integration technique but you don't benefit from joint learning. So yes, the only answer I have to that is that a number of those multi-view algorithms I've talked about. So some of those were already, I had already spotted it built in like this with, I mean, this fancy lasso or the graph-based methods where nodes are features. So those were graph-guided feature selection but also, for instance, for NMF or for CCA and you have sparse variants that allow you to build sparse models that are only using part of the features. Of course, here, for all those approaches, you have this issue that sparsity here is achieved with L1 regularization and L1 regularization is pretty unstable. So if you have many correlated features to start with your L1 regularizer is going to select, I mean, it's not going to be stable in the sense that if you have small variations in your input data, so you remove one sample you run it on a different day on a different machine this type of thing is you get different sets of explanatory features in the end. And so this is a bunch of approaches that try to address this in particular that is also one of the reasons for developing all those graph-based feature selection approaches to in order to try and stabilize this but we're still not as something ideal. So those were my concluding words. I will share my screen with all the references which are coming, which are the last slides of the talks. But now I've been talking for a very long time and I'd like you to ask me questions. Thank you very much, Chloe, for this excellent talk on data integration, we've got a very holistic overview about the various approaches that exist. I'm sure there will be questions. Let us start with Dejan. Thank you very much for this presentation. It's going to be very useful to me in the new so I'm extremely happy to have the possibility to attend this lecture. My question is, do you have any recommendation when performing data integration with one of the data type being images and especially are there some graph-based methods that are particularly relevant or commonly used for the type of data? Okay, so this is something I know very little about but I know that there's deep learning approaches that I've been trying to address this and there's in particular, I think it's this paper as well, I can send you the reference later on. It's called page PAGE. It's not really easy to look for a paper called PAGE on Google Solar where they're doing is, I mean, it's this idea of no knowledge and from neural network architecture and they're combining this with the multi-view deep learning I was showing before. So they have this on the one hand and then they have a different inputs that would be, that comes from whole slide images and the classical, I mean, what now passes for classical for deep learning on whole slide images, network here and in the end, it joins the different views. I think that's so, yeah, I don't know. So this is in particularly tying together the different information right between the omics and the images. People in spatial transcriptomics have been trying to do this but I'm not really aware of a particular reference here. I'm gonna look at this paper, thank you very much. Sure thing. Thank you, Jan and Chloe. Giovanni is next. Hi, thank you for the talk. I really appreciate it. I have a quick question. I hope it's not something that I missed from the talk. So my question is to many complicated models like something like neural networks, they're often trained with gradient descent or some similar optimizers, but a lot of the constraints that you mentioned in some of the models you presented are not differentiable. Like I remember there is the rank of a matrix in one of the models that you presented. Is there any general way to try to overcome this challenge if we want to use these more interesting constraints for some model that can only really be optimized to create the descent for... They're typically optimized with gradient descent. So I mean, it's just, for instance, the L1 constraint, it's not differentiable but you pretend it is because it's convex and it has only one point of non-differentiation. I would look into how, I mean, this using rank constraints on lassoes is a fairly common thing to do. I would look specifically at how the problems are being solved and I'm pretty sure you can translate this to a neural network approach. But it was not something I had talked about and I would also not be offended as you had missed one second of information in all what I've just delivered. Thank you. I have further questions for Chloe. I have one question. Oh, no, Pellin has a wand. Yes, Pellin says she has problems with her mic and would like to put it in writing. Yes, that's no problem at all. I'll read it out to the question. So Pellin is asking, thank you for the talk. It was very informative for my side. My question is related to a deep multi-view learning on slide 12. May I learn what is the difference if we first combine multi-views and obtain the encoding information by using autoencoder rather than getting the combination of each last hidden layers of each model of each view? So are you talking about like, what would be the difference between this and just concatenating the different views? Or did I miss something? Yes, yes, so Pellin confirmed that this is the answer to your question is yes. Okay, so I think the difference is that having those intermediate layers that are specific to each view allow you to address this limitation that was talking about, about combining apples and oranges. As you see those different views, they don't live in the same, you know, they're not in comparable scales or those type of things. I think having first some layers that are specific to each view and then something that's global allows you to sort of first homogenize the data in the different views and then learn something global. I hope this answers the question. Yes, thanks. She says yes and thanks. Okay, thank you. Great. So my question is the following. We talked this morning a bit about confounders in Magnus's talk and this whole feed of data integration could also, well, on one hand it may benefit the study. On the other hand, the more sources you integrate the more risks there are that you integrate some confounding or that there is some systematic difference between data views or data sources that introduce some signal into your joint data set which is not biological but somehow technical or confounding related. So do you see or do you know methods that address this problem or do you think this could be a line of future work in this field or how would you go about this kind of confounding risk in data integration? So this is a very interesting question and I'm actually not entirely sure that you have more risk of confounding here. I mean, with multiple views and in single views would actually tend to hope that some of the technical biases if you don't have the same technical biases or different views, maybe hopefully you might have things that compensate each other or maybe it's even worse because if you've acquired your difference, I mean different data differently across different views then it's just you're only going to learn technical bias. I'm not sure. I don't know if there's something specific to multi-view to do or if it's just the same problem as when you treat each view independently from each other. Yeah, I don't have an answer to your question but I'm not familiar with any work that would be specific to confounders in multi-view learning but I'm also not, you know, I mean I'm not familiar with it, but it might exist, right? No, I agree. I mean, there's not really, I'm also not aware of work that is specific to the multi-view setting. I mean, there's a lot for the single view. Yeah, yeah. But I'm not sure if there's anything about the multi-views that would allow you to address it in a different ways than in the different single views. I think maybe some of the algorithms that address it on single views would need to be ported to multi-view. Yeah, I think one has to think carefully about how related these confounders then would be about in the different views and if they cancel each other out or if they only exist in one view I think it depends very much on this. Yeah, why they just accumulate because they exist in all views. Yeah. Exactly. Definitely. Okay. Thank you. Interesting. Are there further questions for Chloe? Can I just jump into this? Falka, please. So I'm a little bit confused.  I mean, that's the typical case. And the ones where you add too much inputs, I mean, that can happen, but isn't that extremely rare? I mean, it's one of these Perl's conditions, Bechtel or something. One of these strange things that you suddenly introduce dependencies which aren't there if you exclude this variable. But typically the issue of my understanding is that confounding if you leave them out and you have a hidden confounder, that's the problem. If you assume too many inputs, I mean, that might not be nice. And eventually also, of course, might not be very helpful for many statistical reasons, but it's not, I mean, my understanding would be that that is not the big issue. But maybe I'm a sense of it. Well, I think it depends because if you, I mean, if a confounding is due to, you're missing biological information, then of course, if you edit, if you had multiple views and you add more information, it helped. But if you're confounding, it's like due to technical biases. Maybe I don't understand. So imagine you've like measured, you're trying to like cluster, you're trying to cluster different samples from cancer patients and all the different views have been acquired in two different clinics, I mean, two different labs. And so actually what's the major difference between all those samples is whether they've been measured in lab one or in lab two. Yeah, then you should include the identifier for the lab and then you remove the code. Right, so yeah, that's the same. So if you use one, if you use what, I mean, in this case, clearly, if you inquiry this information whether it's single view or multiple view, you should be able to address it. But if you're forgotten to include it. Yeah, that's the thing. If you forget to include it, then you have a problem. That's the problem with confounders, right? He's forgetting to include them. Well, another example to forget would be if you have a genetic data set and you now start adding another population to that data set where the phenotypic ratio is a bit different. Do you have more cases of a particular phenotype when this phenotype may start to correlate with this, with the membership in this additional cohort in this of a different geographic origin. This is a very typical problem in genetics that you have this kind of population structure and then data integration actually harms because the new data source correlates with the phenotype then. But it's the same thing, right? If you include features that capture population structure or you do anything, any other of those population structure correction approaches and... It should be okay. And if you don't do it on the single views, it's also bad for the single view approach. So... Exactly. I think we agree probably, but it was just against my intuition that you said too many inputs can be harmful. I mean, there can be in some odd ways, but in most cases, I mean, I think the simple, I mean, what Ruben sometimes criticizes because I mean, more inputs cannot hurt, which of course is not quite true in this screen-rate view, but the more dangerous thing, I think is to leave something out, which is what I think. For sure. My logic would be, if I may, folk and gluey. So my logic would be that the more you measure the higher the risk that the device that you're using to measure introduces confounding. That then the measurements correlate or that the measurements start correlating or create correlations between the device it was measured on and the phenotype. And because the different views are not, the different views are not measured with the same machine. Again, we see this in medical data all the time. If there's an upgrade to a machine in a hospital, then the measurement starts changing. So if there's a slight difference in class ratios before and after the upgrade of the machine, then the machine correlates with the phenotype, for example, the machine version in that case. Yeah, but the trivial solution, which is probably not a good solution is to include the identifier of your device. Absolutely, but it's often difficult to do this in a holistic and systematic way. So often one misses, then the crucial thing that has changed maybe sometimes it may be just a software update of the machine that measured one view and then you have a confounder you didn't notice here. Of course, also what you mentioned, I mean, one solution to confounding, I mean, one is to include the inputs or the other one is to the stratification or to preferably send also removed. Yeah, but all those techniques are not particularly multi-view specific, right? It's just, yeah. Yeah, maybe the danger becomes, whatever. No, probably not. I guess you could apply a multi-view clustering techniques and then see whether if your clusters seem to you to have more to do with biology or with some technical artifacts. Good, thank you. Then we send a round of applause to you, Chloe, for this lecture that was wonderful. We have a 20-minute break now until we continue with Katharine Röder's talk at three. Thank you, Chloe. Thank you. See you all after a short break.


Confused about CAR T-cells or checkpoint inhibitors? Want the latest on cancer prevention and diagnosis? As immunotherapy takes oncology by storm and technology continues to drive advances in medicine, keep up by joining lab routes for its fourth annual Virtual Cancer Research and Oncology conference this October. The meeting will cover everything from cancer mechanics and prevention to detection and novel therapies, allowing you to bone up on the latest discoveries and trends. Fred Kramer of Rutgers University will discuss advanced molecular diagnostics. He'll explain how real-time multiplex PCR is poised to become the fastest and cheapest way to monitor cancer from liquid biopsies. The technique allows for selective amplification of mutated DNA fragments within tumors, allowing doctors to properly diagnose and tailor treatment. Louise Perkins, the chief science officer of the Melanoma Research Alliance, will cover how scientists are learning to use emerging immunotherapy and precision medicine techniques to battle one of the most common cancers in America. Stephen Hughes, director of the North Wales and Northwest Urological Research Center, will talk about the role of cytokines and inflammation in kidney and bladder cancer. Finally, pioneering chemist and physicist Jacinto Scoles will discuss his latest forays into biomedical nanoscience. Famous for discoveries about van der Waals' forces and molecular beams, Scoles is now working on funneling that expertise into cancer diagnostics. With collaborators, he recently patented a way to count circulating tumor cells from picoliter-sized drops of blood. Other highlights include talks on targeted therapies for advanced cancers, novel systems for monitoring diseases and predicting outcomes, and many symposiums on prostate, breast, and brain cancers, as well as the KRAS initiative. Free continuing education credits will be available. Rarely does a field move so quickly to advance scientific understanding and treatment for patients. In this exciting time, don't miss the opportunity to learn more and network with leading scientists from around the world. Attend Labroot's Virtual Cancer Research and Oncology Conference this October 5th and 6th.


Proteases are enzymes that break down proteins into smaller peptid chains and amino acids. These enzymes are widely used in the food, pharmaceutical, leather and detergent industries due to their ability to degrade protein-based materials. Recent research has focused on discovering new proteases with unique properties and optimizing existing protease production methods. This review summarizes some of the most recent studies on alkaline proteases, including their isolation from different sources, effects of environmental and nutritional factors on production, and enzymatic and physical characteristics. This article was authored by Canupria-Migliani Sharma, Rajesh Kumar, Sabi Panwar and others.


The Woodrow Wilson International Center for Scholars and the Project on Emerging Nanotechnologies developed the Nanotechnology Consumer Products Inventory, CPI, in 2005 to document the marketing and distribution of nano-enabled products into the commercial marketplace. This initiative was driven by the need to identify and track the increasing presence of nanotechnology in everyday consumer products. The goal was to create a reliable source of information about the products containing nanomaterials and to provide a comprehensive overview of the current landscape of nanotechnology in consumer products. The original inventory was updated in 2013 with the addition of eight new descriptors for consumer products, including information pertaining to the nanomaterials contained in each product. Additionally, the project was motivated by the recognition that a diverse group of stakeholders from academia, industry, and state-slash-federal government had become highly dependent on the inventory as an important resource in bellwether of the pervasiveness of nanotechnology in society. Interviews were conducted with 68 nanotechnology experts to assess key information needs. These responses helped guide inventory modifications by. This article was authored by Marina Evance, Todd Coykin, Eric Pevegerano, and others. We are article.tv, links in the description below.


Ahead of the 2023 presidential campaigns, the CYG group tag has begun the constitution of its state chapters in the South South, urging them not to leave any stone unturned at the battle to ensure that Borla Tinibu emerges victorious at the polls. At the inauguration in River State, the group took the pro-APC campaign to the downstairs of non-natives, giving them reasons to vote for the APC at all levels. Our correspondent, Jesse Essay, completes this report. With a visit to the Eurobank community in the state, later on to the House of Fulanes, the mission is to see Tinibu elected in 2023 as president and Tony Ecol as governor of River State. After a favorable response from over 10,000 faithful visitors, the team proceeds to commission those who will be in charge of leading the APC to victory in the state. And we have created all the coordinators who created coordinators at state level to take responsibility within their individual states while they support them at the federal level. As you can see, we made a great choice with our Honourable Honourable Ruben, an amazing man. Members of the Ruben Initiative for Good Leadership, and Accountability are also commissioned and they say they are ready to deliver. Before now, already we have put all our house in order and APC is going to be victorious come 2023. I'll show you that. And while we are in, you have seen the calibre of people. Me around the unit to unit, we are going around the world to world. And then I can't show you that with the support and conjunction with the River State, we will come out victoriously. For this dream to come to fruition, the grassroots has to be covered and this is their drive. We are ready for that. The South states are ready. We are ready to take over. We are ready to do anything to make sure, not by violence, not by violence. We have trust lives. We have work. We are meeting people from the grassroots.  We have cognitants from the works with the Centre. In the South states. You are seeing a grassroots movement. So when you ask how I feel about the past six and eight months ago, this is an innovation that will count on the nation where people are right. With high expectations from the 2023 elections, the groups promise to reach more areas, giving a legible vote as reasons to support the APC. Jesse. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


The study investigated growth, photosynthetic performance and nitrogen status in leafy herbs grown under different light spectra emitted by LEDs using fluorescence-based sensors to detect crop and status and maximum photochemical efficiency of photosystem 2. For light treatments including dominant red light and or white LED lights at 200 mole M2S1 at plant level provided the most favorable conditions for plant growth and development while a high proportion of blue light resulted in higher chlorophyll index and nitrogen balance index, NBI, than under dominant red light treatments. The study highlights the potential of fluorescence-based sensors in photobiology research as well as in the production of leafy herbs under LED lights. This article was authored by Bozina Matysiak and Artur Kowalski.


Hi everybody it's Sam at Mixed Up Craft, thank you for watching my tutorial today. I'm going to be showing you how to make this really rather chunky mini album and this is using six by six card envelopes. I have so many of them, they won't all get used because I make a lot of my envelopes or you know I make box envelopes so I just found myself having so many. I thought there's got to be a way to use these so I have come up with I think quite a nice fun way to make the envelopes as your pockets and created this really cool album so as you can see this is Malaysia there's no photos in it yet because now I've made it I can go and order them so on these two rather large ring binders so I will or binder rings even and then obviously they're probably in the way a little bit they're decorative more than anything I'll probably push them around a bit more but as you open it up you have all these pages so I'm going to show you how to make one page and then you can make as many as you want but on each page you have two of these mats or photo mats here to put four by four photos on so you can have four photos there and then you've got a little tuck spot to pop them in and then on the top of the page you have this photo mat here and then because this is made up of two envelopes you have the first envelope has this mat and layer and then the other envelope at the back has this mat and I say mat and layer that picture mat and then you also have this area here and you could do another talk spot on the back of this one but each page here will hold 11 photos and I have eight of them and then if you did do those and these I roughly worked out you could fit about 100 photos but you could easily do more as well if you wanted to so it's a really lovely size it's just over six by six oh no seven by seven this one just roughly about seven by seven it's a bit again you can use any envelopes you can use you know as many pages it really is something that you can adapt you know however you want I've gone and protected all the corners there I love all the hardware like I said I've got my little charm there I've got feathers little flowers I've tried to really kind of bring the feel of Malaysia onto that front page and it's helped because I've used the tropical bliss this is the paper addicts and it's gone now all I have left are these scraps that's it so you're going to see that all get in use now in the video but I will link this below and I did use a little bit of botanical garden I think it was it was an old fester edition one again I'll link that as well but yeah I love this and I hope you do too and let me show you how to make it okay so the easiest way to do this is to break everything down for what you need for every page so first of all I'm just going to show you one page and the quantity that you need to make that page then you can decide how many pages you want and then you can decide how many of those sizes I give you you will need I will list in my blog all of the sizes and quantities of what I used for this album but I'm going to do the tutorial just showing you one page and you can just work from that so as I said at the beginning it's using six by six envelopes but it's very easy to adapt and change it to any size envelope so here are my two six by six envelopes well I say six by six they are just over that one six and one eighth so it's yeah it's about six and one eighth by six and one eighth but that's what I'm using that the you know packaged as being six by six envelopes very straightforward what you want to do is open both of them up and you're just going to feed one inside now it's up to you if you want to stick some glue on the back of that one so what I can do here is just uh to grab that one because that one's nearly run out you just put a little bead just there I'm not you know I'm not too worried about anything else because it won't catch so and just stick that inside you just want to make sure they're just the joints marry up here like so you can see there they just meet and then if you flip it over you've got this one just hanging around fold it back on itself and it will have that gum from where you would you know make it wet to stick it if you were sealing a card just add some glue all over that triangle piece and stick it down okay so really nice and easy and then fold the whole thing back over again so you've got a pocket on this side here and then a pocket on the front here and then that is going to form so the open side here is going to be the inside the left hand side where the binder rings will be this is the outer side where we'll have those two pockets that I showed you at the beginning so what you want to do is open it back up so you've got the two open parts of the envelope facing away so underneath and then just with some tape here I'm going to run this is half inch okay so I wouldn't go any bigger than that because the mats that go inside I've made sit with this half inch tape so if you were to put say like a thicker one inch then the mat probably wouldn't fit and you'd have to change the sizes for that as well so I'm just going to cut there and then you want to run another strip along the open side okay so I'll just show you again you're only doing it on one so we're going to turn it over so there again you've got your open side and your open side and that's on the left hand side and then running along the bottom here and up here I've added my tape so now if I take the backing off and then just seal that up okay I now have a pocket at the top pocket here and a pocket here okay and it's going to have the sealed end so I've sealed it there and there like I said the two open sides and the pocket at the top that's how you want it to be so you want this sealed side along the bottom and the side here on the left okay openings here on the right and that one on the top then it's down to decorating so you need to do that as many times as you want you know however many pages that you want okay so like I said I've done eight pages so you would need to you know do that seven more times so now our mats and layers so I have these pieces here so I've got this one and it's going to have that strip there and then on the other side it will have this one and that strip there now when you stick these down because obviously what we don't want to do is I glue all on this and stick it down because you'll end up sealing up your pocket so all I've done is just get some glue and just roughly stick it just cover the area of this here okay so I haven't put any glue on this piece you've got to keep that open because that's your pocket so then I can just stick this down okay and now I've got the pocket like that okay so you don't see that triangle on now if you want to keep that as a feature then you would have to cut your pattern paper accordingly but I just wanted it to be covered all I'm using the envelopes for is to quickly have pockets it's as simple as that I've got so many of them like I said and yeah they're just never getting used because I make a lot of them for my kind of you know custom cards or more dimensional cards so and then these here and I will give the measurements to these okay so for each pocket you will want two pieces that are five and a quarter by five and seven eights and that's pattern paper for your outer mats your main mats so you want to make sure that you have your pocket on the right and your strip on the left on that sealed side so when I flip it over it's easiest to do it that way keep your pocket on your right hand side and the strip on the left there so like I said that's going to go there and that one's going to go over there so I just want to add my glue same way again just roughly covering that area where the pattern paper will go and this kalal glue is really good because it dries nice and stiff so it does strengthen this album even more so because it's got so many layers to it anyway by having this this glue as well it just it does make it a really strong piece and then these strips here are half an inch by five and seven eights so the same height you could have them a bit thicker if you want but I just like that border of the white so again just stick that one down okay so that is now my covered pocket so you want to make sure that you have it although you want to keep this on the left hand side you want to make sure that you have that pocket at the top otherwise if you go to put your whole punches and your pockets at the bottom your mats going to fall out so that pocket needs to be at the top these two openings again on that right hand side and this on the left and that is one page so now it's up to you if you want to add the extra little tuck spot that I've done on the front if you do you want a square okay so this is this was a three by three square and then I just cut it on the diagonal to create this and then you can use that for your other pages now I have only done it on the front of every page but you can do it on the back as well if you want but at the minute I'm going to keep that plane because I may just want to do one large photo or I may add another pocket at a later day and add more mats in there so for this one here and then this pattern piece was from a two and three quarter by two and three quarter square again cut it on the diagonal and then that will sit nicely over the top with a slight little frame around it so I'm going to stick these two down for the little tuck spot I'm just using a very very thin red tape because you don't want to add too much adhesive because you want there to be room for the smaller mats to actually tuck into this area so if you add you know too thick a tape or a wet glue it might end up spreading more into this area and you won't really have much of a tuck spot so this thin one-quarter inch tape is good so now I'm just popping that right down did I have it on the right side I know it was meant to be on that one to compliment that oh well never mind this is the last page so and you're not really going to see this paper once the mats are over it okay so that's my little tuck spot then for my little smaller mats to go in this one here you want two pieces of four and a half by four and a half and then you want four pieces of pattern paper mine are all different and these are four and a quarter by four and a quarter and then the idea is is that you can have a four by four photo on all of these so you can get four photos on those so each one will stick here and here and then they're all popping that little tuck spot so again I'm just going to get those stuck down okay so there are my two mats now ready for photos and they just sit nicely inside like so okay now bearing in mind all of that that I just showed you was meant to go against this so the colors were all in with that one but it's just typical the one I go to film I do the wrong way but um yeah it's just it clashes a little bit but it does all still work because it's all from the same well some of it's from the same pack anyway so that's that piece done and then I have your mats now for the top pocket okay so your mat for this one here I'm only having one mat but you may want to do more than you know the one so this is a piece that is five and one eighth of an inch by five and three quarters and then I've got these pattern papers to go on each side which is four and seven eighths of an inch by five and a half so I've got one going there and one going on the back there but before I put those on I have these which I've just punched from my one inch circle punch and oh that's got some double-sided obviously cut that from something else which is even better so and basically I'm just sticking half of it over like that that had some double-sided tape on it obviously from another project and then I'm just going to add some glue on the other side and just stick that over now if you want to keep it just like that and have them plain and then your photos go straight onto this but then I stuck my pattern paper over the top all right so I'm going to stack those all down okay so now that is all ready to go in the top there it should poke out like that then for your pocket so you will need all the pattern paper so you want two pieces because you've got two pockets so you've got that front pocket and then you've got the pocket on that one as well so you want two pieces that are five and a quarter by five and three quarters and then you'll need four pieces of pattern paper so get rid of that one so I've got mine here so four pieces that are five by five and a half and they are going to go on each side like this one on each one and then the other two on that one and again before I do that I've got these again but this time you want to put them on the side because these mats are going to go in like this so I want these here so it helps me to pull them out so with that one you put them on the top but on these ones you're going to put them on the right hand side so again I'm just going to add a little bit of glue and then just pop it down on my grid here and you just want to get it as centered as possible you can always obviously move it like so and then I'll just pop another bit of glue there so so I'm going to do that again on that one and then I'm also going to stick down all of my layers okay so that one can go in the front pocket and then this one in the back pocket again you can double up on them if you want to but I like them with just the one okay so that's one pocket so nice and easy to do you know this doesn't really take too long to do it the longest part is cutting all of your pieces putting it all together really quick so it's up to you now how many of these pages you want to do and that's the nice thing about having a ring kind of binder a ring binder album because you can change the size of the rings accordingly so if you do end up adding more to it you can so I've already gone ahead and got all of mine done and my front because I'm going to show you how to do the back and I've got these huge great big rings so now we need to make our kind of holes to attach it so I'm going to do that now actually what I do is leave that on there for now and we do the back cover because it's easier to decide where you want your holes to be with the back cover so I'm going to leave all that to one side you're going to want two pieces of chipboard or grayboard mine is the 3 mil and I got it from every cross of pound and you want two pieces because one for the front one for the back that are six and three quarters by five and five eighths and then you want a piece of pattern paper that's eight by eight okay so I've gone for this nice color here I might end up having this as my front it depends with my decoration that I'm going to do okay so what you want to do first of all is just cover your whole piece in double-sided tape everybody's got their own way of kind of doing their covers for I guess mini albums and I like to cover my whole area with double-sided tape first you can use double-sided sheets if you've got them then I'm going to flip this over and I'm going to add some liquid glue just over this as well like I said just for good measure and then you just want to sit it in the center of your paper there just roughly like so and then I'm going to turn it over and just make sure that's all nice and stuck and there's no air bubbles okay and then if you just go around each side and just bend that pattern paper around okay then I'm going to go and add another strip of double-sided tape along all four sides you don't want to add it on the side you can add it on all four sides of the actual chipboard but it doesn't really matter okay and then you just want to cut off each corner but you just want to leave about a quarter of an inch there from the corner just so that you get a nice corner but I'm going to be covering mine with those metal corner protectors so I'd never worry too much about what my corners look like because I always I don't think I've ever done an album without putting those corners on because it just helps it last much much longer and keep it all protected so I'm going to take the backing off of two opposite sides first okay I'm just going to run a bead of glue right in the side there because this is three mil there's quite a nice thick side to this chipboard so it will take to the glue then I'm just going to bring up the side with my bone folder so it should start to stick and the glue should ooze out the top so once you then start to wrap this all the way around that glue will just spread over more so once it's kind of stuck in place start to bring it over glue kind of coming out the sides there okay so that's that one and then do the same with the other side okay so you've folded them over and then you'll see where you've folded over this is overhanging you can push that with your nail like so so I've pushed it in to the side there so that when you fold that around you get a nice finish okay so you can also use your bone folder just to push down against it to do that on the others and then again just going to run some glue and then just bring that up and stick them down exactly the same way that you did the others okay so there is our front or back you'll have two of these okay so like I said I'd already gone and done one then we want again you'll need two pieces of just plain paper you may want patterned again it's all up to you but this is six and a half by six and five eights and this is going to stick over this one here just leaving a very very small frame okay you can see that so I'm just going to stick that one down okay so now I've just got this ready next I'm going to add my decorative corners so I will link these ones if I can find these exact ones because they do change a lot and I know some of the links I shared before I checked them and then they've they're either not in use anymore or they've changed the style so but I should be able to find these ones but basically they go on each corner like so so you've got your decorative piece at the front and then you just got that simple part at the back so I'll do one at a time I will silence the video when I start hammering you can add some tissue over the top if you want but what I like to do is just hammer the corner first and then work your way around each side okay there's that one done every time you hammer always hammer into this because you want to be pushing it into this part okay so I'm just going to finish all of those corners so I've done it so that you should line this up with my case it's with that white paper okay and then the bottom along with the white paper as well and then this should come in just in line with that white paper there and then this should overhang the top okay so that's where you want to keep it all kind of in place now because I've already done these ones here on these I came in at one and three quarters yeah so I'm just going to use this one that I've already done and sit it over the top here so it's up to you where you do them to be honest where you punch the holes it really will not make any difference as long as they're equal from you know each side so I'm just going to grab my pencil and then I've got my cropper dial the cropper dial is handy for this because you're obviously cutting through a lot of layers but um there are you know plenty of other hole punches that will punch through as well so you might have to just be a bit more forceful so now I've just done them and then these rings here are huge they're the biggest ones that I've purchased and these are three inches in diameter okay and then with this one now all I can do is just line that all up so I'm happy that it's all where it needs to be and then just use that one as a little guide put that one on there and then with this one again I can just use my hole punch and just line that up and then pop that through seal this all up okay there you have it it's huge this is a whopper and I absolutely love it and it fits perfectly with these three inch rings so now it's the fun bit of decorating so I have got here some pieces of ribbons some of them are from when I've been traveling I've got this little charm here which I picked up somewhere in Malaysia and then I have these here which I've already gone and fussy cut from the paper pack and these here I've die cut from that beautiful bright rosa fern border die set so you'll see here I have showed this before you get these massive beautiful fern dies so you can see how big they are there then I've also gone and prepared a doily here I've just covered it in some brown just because I wanted something to just kind of back it and then I can start layering on top of that and then I have all these flowers I have little sprigs I have more leaves bits and pieces here and I also need to get my title which is just going to be Malaysia and yeah start building this all up so I'm going to pop it on high speed and then I'll talk you through what I've done at the end is my front cover all finished so my camera stopped recording as always when I'm doing these kind of things because this obviously took a bit longer and I did stop and have a drink but I stamped Malaysia and then I just kind of distressed the paper because it was too white and used my distressed tool around the edges and I've got these little flag stamps so it's always nice to pop a little flag next to where you know the country is that I've been to and also I may have a specific year for these photos so or I might combine so I will also add a year there as well so it's a little bit like a scrapbook layout on the cover and then I've got here that little dangle and then just cut bits of you know these ribbons and stuff there because yeah they're just bits that I've kind of picked up I think yeah that might have come from a temple or something it's just stuff just yeah little reminders there which I think tie it all together really nicely so this is a bumper piece it's absolutely gorgeous I can't wait to fill it and I will do a flip through I am gonna do like a mini album flip through video so I will kind of show you all of my mini albums and then you know you can refer to the video and choose which one you want to make if you want to so yeah that is the tutorial so it should be nice and quick I wanted to keep it as short as possible because then you can go away and decide on you know how many pages it is that you want to do I'm also going to probably pop something on the back there and any kind of tickets and things that I might have so it's a little bit like a traveller's journal as well in a sense because I can add them onto the front and onto the back but I'm really pleased with how this turn out it stands up as well so I can keep it displayed on my shelf and yeah and look at it when I want to so I hope you've enjoyed this mini album tutorial today if you have please give me a thumbs up and subscribe to my channel so you get to see more thanks for watching bye