introductions that is now time for members, statements from the member from the P. N. Carlton. Thank you very much Mr. Speaker. Each day we have a few minutes dedicated to this assembly for each of our caucuses to talk about important people in their constituencies. I actually think it is a shining example of how we can bring the stories of incredible people from our constituencies into the public record of Ontario and that is what I want to do today with Mr. Raymond Desjardins from the Bar Haven Legion. Ray is going to turn 75 years old on Saturday and he has been a great friend to me and to many others across the community as he has championed the beginning, the inception of the Bar Haven Legion, one of Canada's most successful legions, that just began its institutional history in Nepean just 10 years ago. Ray is the pod ray of our Legion and he has presided over many difficult passings and circumstances in our community, always fighting for the veterans who served our community but going one step further by actually being a very important supporter of the Purly-Reedo Veterans Centre and ensuring that all of those from our community in Nepean Carlton are treated with dignity and respect. He has been part of food and toy drives, he has been part of the annual Christmas stocking filling at the Purly-Reedo and it is for all of these wonderful contributions that he was awarded the Order of Ottawa by the City of Ottawa and he is a Knight Commander of the Order of St. George. On behalf of all of his friends in Nepean Carlton but more importantly to all of those people that Ray has volunteered for and has given so much to, I want to say happy birthday to you on your very special day, thank you. Thank you for the number of things, the number of things that I am grateful for. Thank you Mr. Speaker, it is with a heavy heart that I rise today to talk about the loss of the icon in my riding, Mr. Don Ead. Don was the guy in Chippewa who is involved in everything and knew everyone. He and his wife Carol volunteered with countless organizations including the Willoughby Historic Society, the Village of Chippewa Citizens Committee, the Niagara Falls Battle of Chippewa and so many more. Don and his wife Carol were recently inducted into the Niagara Falls Arts and Culture Hall of Fame. Whether he knew him from his days as a champion pigeon racer or friends he had coffee with every morning in the Chippewa Tim Hortons or a person who came to see his collection of historic items from the Village of Chippewa or someone who got a Chippewa green card from him, everyone loved Don. They loved him so much that he called him Mr. Chippewa and from the Village of Chippewa or someone who got a Chippewa green card from him, everyone loved Don. They loved him so much that he called him Mr. Chippewa, a name he proudly put on his license plate to go with the Chippewa Ontario Winter Car Sticker, Window Car Sticker. Today would have been Don's 79th birthday to be exact and on this day I'm sure that wherever he is he knows that his friends, his family and his community will miss him dearly. I met Don during my first campaign I ever ran and he stood by me every campaign after. I honestly say that without Don's support and his incredible friendship I wouldn't be standing here today to give this statement. For over 15 years Don has been my dedicated and loyal friend and I know I will never stop missing him.  Don eat. So long Mr. Chippewa and thank you for being my friend. Thank you. The members, stay with the member from Scarborough Agent Court. Thank you Mr. Speaker. I rise today to speak about the third annual model parliament a three-day educational program designed for grades 10 to 12 students who are interested in current events and provincial issues. This unique educational experience bring 107 students from across Ontario representing each of the province's writings in Toronto. The model parliament is an excellent form for our youth to gain a stronger insight and knowledge of the problems of parliamentary practices and traditions while participating in an authentic experience that will teach them the value and importance of a democratic process. Tomorrow Mr. Speaker the participant of the model parliament will be doing mock debate in this legislative chamber. And I look forward to attending this debate as I know it will be lively and engaging. Last night I had an opportunity to meet many of the student participants attending the 2016 model parliament including my students Anna Wang, a grade 11 student at Dr. Norman Patheon Collegiate. She's from the great writing of Scarborough Agent Court, a highly respected young activist recognized by her peers and worked very hard on social and global issues. Mr. Speaker I know the 2016 student model parliament will forge new friendships, acquire new appreciation about how decisions are being made here in this legislature and become active citizens. I want to thank all the staff at the legislature for organizing the third annual model parliament. Thank you Mr. Speaker. Thank you. Thank you Speaker. Last night I had the pleasure to attend the Life Sciences Ontario annual awards gallon. Life Sciences Ontario is a member driven organization that represents and promotes the province's vibrant and diverse life sciences sector. Here are highlights of some of the awards presented last night. A lifetime achievement award was given to Murray McLaughlin, the executive director of Bio Industrial Innovation Canada. This award recognized his leadership in commercializing biotechnologies and growing innovative based companies. The LSO volunteer award was presented to Jason Laughlin who is a long standing member of the LSO board and whose advocacy and enthusiasm for the life sciences emanates in his role on many committees and initiatives for LSO. And finally the Life Science Leadership Award was given to David Allen, a board member of Formation Biologics. I met David in my office back in December on the LSO lobby date saying that David is passionate about furthering the life sciences industry as merely an understatement. He zealously champions the need for public policy to foster and promote the same high tolerance for capital investment in life sciences which Canadian investors practice in other industries. David advocates for the adjustment of fiscal obstacles to life sciences capital and I must stress that I agree with David on this point. There is more to be done in the life sciences sector in Ontario and as the critic for research and innovation it is my goal to do that that I can advocate and promote the needs of organizations such as LSO. Thank you, Speaker. Thank you. Further member statement? Member from Hamilton East, Stony Creek. Thank you, Speaker. Speaker, it isn't too often that we find a subject on which both labour and management agree where union and non-union workers are on the same page. Yet with the release of the Dean review on the Ontario College of Trails there appears to be a strong collective voice for many in the construction industry. While the Dean review contains many positive elements there is concerned that the government is moving too quickly on some of the recommendations. Recommendations that could have profound impact on the construction industry in our province. I remind members that when the college was set up the objective was to allow the construction industry to regulate itself through an independent self-financing body. Now with some of the recommendations in the Dean review it appears that the government is changing the original basis for the college. Earlier this week I met with members of the Progressive Certified Trails Coalition where holding meetings to explain the impact of the Dean review on health and safety, apprenticeships and public safety. I hope all members have an opportunity to chat with them, learn more about these important issues, and ensure that the government holds detailed and inclusive consultations for implementing the recommendations of the Dean review. Thank you for the member from Eglinton, Lawrence. Thank you, Mr. Speaker. Today I want to give tribute to Mr. Toronto, Al Green. Al Green basically built this city. He built over 100,000 suburban homes, apartments. He built commercial units. Where you see concentration near the subways on Davisville and Young, down on Bloor and Sherburn, all over the city, Al Green had a vision of people living near public transit. So where the subway went, Al built. Al started on Major Street just a bit west of here. Him and his father Lipa and his brother Harold. They started cleaning chimneys, sweeping chimneys. Then they started repairing chimneys. Then they started building houses. Then they started to build this city. Al Green. And Al Green not only built the city, but he built the arts in this city. He gave millions away in philanthropy to the arts, to sculpture. He also created the Al Green Resource Center for adults with learning challenges. Mr. Al Green also created the Al Green Theater, the Al Green Gallery, the Lipa Green Center for Jewish Community Services. Al Green was everywhere. And everywhere in this city you will see a city that Al Green built. So when you look at Toronto, you're really looking at Al Green's dream.   And we will always remember the building that you did with your own bare hands. Long live Al Green. Thank you. Thank you very much, Mr. Speaker. And I'm pleased to say today that what's uniting us amongst all parties today are reflections on remarkable citizens and I'm going to be following suit. Because earlier this year, Speaker, I was honored to welcome to the Teeswater Town Hall 13 remarkable volunteers from across here in Bruce have worked selflessly to make their communities more vibrant. The evening was one of recognition and celebration. And as one reporter for the Walkard and Harold Times said, this was without any understatement one of the most inspiring events I've ever attended. The remarkable people that were recognized that evening have been truly quiet champions for their communities, just getting the job done. And honestly an inspiration to their many neighbors, friends, and family members that were there in attendance that evening. Whether it had been working for a new community soccer pitch, serving children nutritious food at school, or ensuring people living with disabilities are immersed in our communities, these remarkable here in Bruce citizens have dedicated their time and energy to making their communities better for those around them. And that is why today I say thank you and congratulations to Jeff Roberts of Walkerton, Bob Cullington of Brussels, Kathy Pennington of King Cardin, Vicki Culbert of Godrich, Don Farrell of Ripley, Dr. Chandra Tepathy from King Cardin, Heather Fruick of Brockton, James Rice of Tiverton, Roger Lewington of Bayfield, Gene Culleton of Teeswater, Jenny Rowe of Exeter, Clarence Kiefer of Walkerton, and Diane Lieber of Godrich, formerly South Bruce. Thank you for all you've done for your communities. Thank you speaker. Thank you. Further member statements? The member from Durham. Thank you speaker. I am glad to rise today to welcome the first Syrian refugee family to Skugag. They arrived last week on their trip from Jordan to Montreal and then to Skugag with the help of the Port Perry Refugee Support Group. Of course they happened to arrive on the coldest day of the year so far. But I know that the residents of Skugag generously came forward with meetings and winter gear for them. I want to acknowledge the residents of Durham for their kindness and welcoming, in welcoming this family and preparing to welcome, and they're preparing to welcome several more. I know the residents of Durham value a diverse and inclusive community and are eager to offer their hospitality and to share our vibrant community with those who need our help the most. Thank you to the the Port Perry Refugee Support Group. Thank you. Further member statements from Burlington. Thank you Mr. Speaker. On January 17th I was pleased to join over 100 residents of Burlington and a five kilometer walk to support the resettlement of a Syrian refugee family in our community. This walk was organized by Olivia Walker Edwards a grade nine student from Burlington's Glyde Academy. What started out as a school assignment turned into an event that brought with the best in our neighbors and united our community. Working in partnership with the Burlington Downtown Refugee Alliance, a coalition spearheaded by St. Luke's Anglican Church and made up of faith based groups, community organizations, city hall staff, local businesses and Burlington residents. Olivia helped to raise over $5,000 to support a privately sponsored Syrian family. Olivia's efforts to make this walk happen are proof that one person no matter what their age can make a difference in the lives of others. From speaking to young people in our city including those on my youth advisory committee I know that these young people have a great deal of empathy for the children who are building a new life here in Canada and Olivia embodies this empathy. When I asked her why she chose to do this walk, here's what she said. I organized this walk because we have all been touched by the images we have seen of the children and families leading Syria. I wanted to do something to help and to make a difference in this walk. I have learned that the family we are expecting is very similar to my own. A family of six with two boys and two girls and it makes the walk even more meaningful to me. I look forward to meeting them, she continued and to sharing the success of the walk with them. I hope this will make them feel welcome and supported by their new home. I'd like to congratulate Olivia and thank her and all the students of Blythe Academy as well as the Burlington Refugee Alliance not only for this great event but for their continued efforts to help Syrian families in their community. Thank you Mr. Smith.


Hi, everyone. I'm back. Okay. So, welcome to the last day of the Pathway Workshop. It's nice to see everybody. So, today I'm going to be talking about gene function prediction. Really what I'm going to be talking about is a specific tool called gene mania. And what we designed the tool for, that we, being myself and Gary Bader, is we designed it as a way of giving people access to all of the genomics and functional genomics and proteomics data that's been generated over the last 10 years for the model organisms to make use of that to help you in your research without having you to have a PhD in computer science, essentially to use it. And so, how did we do that? So, first I guess maybe I'll tell you about the outline. So, the outline is, the first thing to understand is in order to use all this data that's been generated, we need this concept of something called a functional interaction between genes. And that concept remains the same across all the different types of data that's been generated over time. And we'll go through all the sort of different types of data that have been generated. And once you have defined this concept of a functional interaction network, then you can say things about predicting gene function, right, either for a single gene or for multiple genes. And that's what we think this data is most useful for. And I'll show you some ways in which you can make use of the data in your own research. Right. And so, there's some concepts in gene function prediction that I want to quickly go over, guilt bias association, gene recommender systems. And then I'm going to explain about gene mania. I'm going to show you a short gene mania demo. We have two things. We have a website for people to use. And that's the way I use it most of the time, because it's easy to use. You can, you know, do a quick query. But if you want to do, like, really hardcore stuff and you want to analyze large gene lists, we also have a cytoscape plug-in, which everybody has already installed on their thing. It has the same functionality as the website. It can deal with slightly larger gene lists. But the other thing is it's integrated into the whole cytoscape framework, so you can use other tools on networks that you generate using gene mania. Okay. And then, so we'll have a lab today. We'll be using the cytoscape plug-in to do an analysis. Okay. And then, finally, there's a few, like, technical details that people always ask questions about that I'm going to explain. Those sort of network waiting schemes will become clear what that is in a second. And then I'm going to introduce you briefly to another tool that does something similar to gene mania called string. Okay. So the question is, how do you use all this genome-wide data that people have been generating over the last decade or so in the lab? Now, what makes this difficult is there's all sorts of different types of data. So there's the genetic interaction data, there's protein-protein interaction data. There's pathways that people have generated. There's protein domain similarity. There's, you know, there's chip data. So there's protein DNA interaction. There's a whole lot of microarray expression data. They're all different kind of data modalities. They have this problem that makes them different than a lot of data that people are used to working with in that they're a little bit noisy. So there's false negatives. There's false positive interactions. And so how are you going to make use of that? And the idea simply is, is that you take each one of these data sets and you define something called a functional interaction between genes. So what's a functional interaction? Functional interaction is evidence that these genes share some function or do similar things. Right? Now that could be a little bit complicated because genes are multifunctional. So they could be sharing different aspects of their function. But if you overlay all the different functional interactions together, what you should hope ultimately is that the genes that really do have a strong shared function will have functional interact... will show functional interactions under a lot of different circumstances. And that's essentially the concept that we're using to make use of this data. I'll make this concept a little bit clearer as I give the presentation. Okay. So, so the idea of functional interactions, well, one of the places that came from is, is from one of the first large-scale microarray data sets. And so what happened here? So this is, this was published, this is data that was published in yeast. And the analysis that I'm showing, the picture up here was from 1998. And essentially what they did is they took a microarray expression profile of yeast cells for all the genes or for some subset of the genes, and those are listed in this heat map down the rows, under a set of different conditions that represents different types of environmental stress. And then they just clustered these expression profiles. And what they discovered was that genes with similar expression profiles were annotated with similar functions. Right? Which was a very nice discovery, right? Because then you can use that information then to say things about genes that you don't know the function of, right? And so one of the ways that you can represent that is represent as, as, as just a network, which gene corresponds to a node. And the links between genes, their weight corresponds to the, the strength of evidence, in this case in the co-expression data that they share function, which you get maybe from correlation. And then the idea being simply that if you find a cluster of nodes that are all very strongly linked together, three of which you have known function for, maybe this one you should say it's cell cycle based on this observation. And then three of which down here you know function for, maybe this gene is involved in protein degradation. Now you wouldn't necessarily say this alone for one microarray data set, but you see the same thing over and over and over and over again that starts to give you confidence that what you're looking at is real, okay? And it's certainly a way of generating hypothesis about gene function. Okay, so when I talked about functional prediction and interacting with all these data in this following way, there's actually two types of questions that you can ask, right? The number one question is what does my gene do? I have some gene, I'm, I don't really, you know, it's shown up in some like screen, for example, maybe it's, it's got a SNP that's associated with some disease that I care about. Can I find out anything about its function by saying, seeing what other genes it has a lot of functional interactions with? Okay, and there's another type of question, and that's if you're trying to say set up a small cell screen, and that's like I have a list of genes that I know have some shared functions, say they're all involved in the wind signaling pathway, can you find me more genes like these genes? Right, and those are two different types of questions. One of those types of questions you can ask the Gene-Mani interface and other what I call gene recommender systems. Okay, so what does my gene do? So the way this analysis works is you take all the network and profile data that's been generated over the last few years, you put it all together in some way, you have a query list that consists of the one gene that you care about, you put this query list together with the network and profile data, you find genes that have a lot of functional interactions with this single gene, and then you take that set of genes and you do an enrichment analysis to find out what functions or what pathways are enriched in the genes that this one is interacting with. And then what I'm showing here is a network that was actually generated on the Gene-Mani website, you can generate similar networks in the plug-in, and then I've just colored nodes by the most enriched function. So here this is whatever that says. Okay, so then the second type of question, this is great in everything, but genes are multifunctional and you might be asking different things about what the gene does. You might want to know what is biological function it's evolved in is, or you might want to know what its biochemical function is. And those are sometimes two different things. Sometimes you might want to know where it's localized in the cell, which does tell you something about its function, but it's not exactly the same thing. You can think of this as like, you know, in terms of how the gene ontology splits up gene function into like biological process, molecular function, and cellular component. But then also there's also, I mean, you know, we know of a lot of genes that have a lot of different functions. So you can think about this in the following way. I came up with this when I was giving this talk in Memphis, Tennessee. Right, so if you just say Memphis, and you say give me more cities like Memphis, well nobody knows how to answer that question until you give them a little bit more context, right? So if I say give me more cities like Memphis, Knoxville, and Nashville, well you have these other two cities which are there in Tennessee, believe me. But if you say Memphis and these Alexandria and Cairo, well then you're talking about Memphis and Egypt. Right, so the list, your query list can define the question that you're asking. Right, so give me more genes like this, and it allows you to ask a more specific question that's defined by your query list. Right, and so again it's the same sort of thing. You take all the network and profile data, you take your gene list, plug it into the gene recommender system, and it gives you the network that connects together these genes. It also gives you genes that are well connected to this gene set here. But now that you've defined the query list, the gene mania or the gene recommender system, it can do something a little bit different. It can figure out what type of data is most relevant to the question that you're asking. If you just give it one gene, it doesn't know what question you're asking. So it has to kind of weight all the data the same way that doesn't depend upon which gene you give it. But if you give it a list, it can say, in this case, microarray data is more relevant than physical interaction data. So let me give you a more specific example. So if you're interested, for example, in a biological process that a gene is involved and say that gene is a gene that responds to salt stress. So possibly the most relevant data to try to figure out what genes are the ones that respond to salt stress are ones where there's a microarray time series that stresses the cell using salt. So presumably some of the genes that are already known to respond to cell stress will respond to the salt stress, and then other genes that respond to salt stress will have correlated expression patterns with those. So in that case, the microarray data on salt stress might be the one that comes up as the most relevant to the question. Now on the other hand, if you're interested in what protein complexes the gene is involved in, well, presumably the physical interaction data is going to be the one that's going to be the most relevant to that question. So under this kind of made-up example that I've come up with, if your query list consists of genes that respond to salt stress, the gene recommender system can identify the fact that, okay, well, what is it about this query list that is the same, and you might be able to identify the fact that they're all co-expressed in this one data set. Or if that query list instead is parts of this protein complex that you're interested, it gives it the ability to identify that what's similar about the genes in the query list is that they're all connected in a physical interaction data set. And so, yeah. So gene mania does both of those things. So essentially, I mean, I'll show you in a second, but you first of all you say, okay, these are the interactions that I think might be relevant, and then of those interactions, gene mania determines which ones are most relevant. So you can control that, but you don't need to. But the thing is, of course, if you provide more information about what you think is relevant, it makes its guessing task easier. So it's more likely to get it right. Okay. All right, so again, here's the interface. I guess this means that this is where I'm supposed to stop and give you an example. Okay, so maybe that's what I'll do. Okay, so here's the website. It's just type in gene mania. And so you get to, right now, we provide this for eight different model organisms. So you can just choose the model organism that you're interested in. Let's say we're interested in yeast for some reason. Okay, in this box, you type in your gene lists. You can also copy and paste over from Excel. I don't want to type in the gene list, so I can just type in genes, and it looks up, and then the check mark tells you whether or not it's actually in the database. We do our best to identify gene identifiers that people use. We don't identify probe set identifiers, but we do try to identify all these gene symbols. We can find entro-gene IDs. We can find ensemble IDs. There's a lot of different gene IDs. Now, but the way we do it, the identifier has to be unique. It has to refer only to one gene, and there's a surprising number of identifiers that aren't unique. The other thing is that it has to refer to a protein-coding gene at this point. It can't have referred to a pseudo-gene. It can't refer to a gene whose status is uncertain. It can't refer to non-coding RNA yet. We're working on that, but right now it's only protein-coding genes that are confirmed to be protein-coding genes that we recognize. Okay, and then you can also, just to make your life easier, you can press this example, and it gives you an example gene list here. Okay, and there's an advanced options panel that allows you to select which networks and what network waiting, and we're going to go over that in the talk in a second. I'm going to close that off and ignore it for the time being, just to give you the example of an analysis. So now I'll press the Go button, and so because I give it a gene list, what Gene Mania is doing is taking all the yeast data that I said is relevant, and we have a default set of networks that we use, and then it's asking what networks are most relevant to the specific question I'm asking by the gene list, and it answers that question by finding the networks where this gene list is highly connected to one another, but not really connected to other genes outside the list. Right, and then it waits those networks based on this measure. The networks are grouped into different types of data, so these are genetic interactions, these are physical interactions, this is other, so there's other types of data here, because these are genes that appear often together in PubMed abstracts. These are genes that respond similarly to chemicals. You can find out more information about where the network comes from by opening up this box and clicking through to PubMed, and these weights reflect the relevance of the genes. Okay, so now here's your network. The black ones of the genes were in the query list, the gray ones are the other genes that are most connected to those, and if you open up the function tabs, what it's done is it's already done an official exact test for you to find gene ontology biological process categories that are enriched in this gene list, and then you can color the genes according to these categories. Alright, so now that's the basic way that the interface works. You can also find out more about the specific genes in the list, and you can go through here, and this tells you all the enriched functions, and by highlighting over the little box, it tells you the name of the specific function. So these are all the enriched functions that this gene is assigned to. These are enriched functions that that gene is not assigned to, but are enriched in the rest of the list. The interface is designed to be fairly intuitive, so you can play around with it, and often it works the way you want it to. Okay, so let's go back and I'll tell you some specific things about the way that the interface works. Okay, so the first thing I did, I showed you the advanced options panel that you can get from the query page. I opened it up and then I shut it quickly. But that's, in the advanced options panel, that's if you want to do... answer your question, you want to only look for certain types of interactions. And in that case, you have the interactions that are grouped by different categories. So there's co-expression, there's co-localization, there's genetic interactions, physical interactions, and so forth. There's also this new type of interaction that we put up there called attributes. And those are the pathways that the gene is assigned to. And you can use those pathways to try to predict more genes like those. Right, and those pathways themselves, they come from actually Gary's lab has collected them as part of the pathways they make available for analysis through enrichment maps. So you open this advanced options panel by clicking that phrase here in the query screen. You can also get it from the network display. And then you can also, there's pre-selected sets of networks. There's a default set, which is essentially all interactions that we trust and some proportion of the microarray, co-expression interactions. We don't include all of them because often they give redundant information. Or you can choose all networks or you can choose none of the networks. Okay. So now, by clicking the checkboxes turn on and off all the networks in a specific category if you click there. Right, and then the fraction here tells you which ones you've selected. If you click through on here, that actually opens up and displays all the networks. Let me, maybe this will be easier to see if I... So here I just opened the advanced options panel from the network display. So here are all the different networks. You can turn the networks off. So there's zero of the one network and co-localization is selected. You can turn all the networks on or you can open this up and click on or off specific networks. And if you click on here, that tells you where the network came from. So you can click through to read the PubMed listing. It also tells you sort of the source of the types of information. And then we also tag the networks based on keywords that were assigned to the networks in PubMed. So you have a lot of choices here. And so this is the attributes. Okay, and these are intrapro attributes. Let's choose human. Let's use the example. So now in human, we have a lot more of these attributes. So these are the pathways. You can look at drug interactions. You can look at microRNA target predictions. And just have fun with that. Okay. So I've already gone through this by showing you it on the interface itself. This is just for your notes so that you see how this works. Okay. So now if there's any questions about the interaction with the website, maybe you could briefly answer them now. And then I'm going to talk to you about some different aspects of the interface which you need to understand, which is how the networks are weighted. Yeah. So you want to upload two different lists? Yeah. Yeah. I would really like you to be able to do that. And this is something we're actually proposing to do. So we're asking for funding to expand it in that way so that you can provide a background list. It's extremely difficult to do that with the web interface. You could try to do that with the cytoscape plugin by just sort of manually removing genes or overlaying genes in some way. I cannot tell you how to do that myself, but if you email me, there's people in the lab who can tell you how to do that. Veronica, do you know how to do that? Oh, right. Okay. Yeah. That's a great idea. Yeah. Okay. So every functional interaction is associated with a weight. So if it's a co-expression, that weight is derived from the Pearson correlation coefficient. So when we compute the final network, we provide a weight to the functional interaction. And then the weight in a network, like the co-expression network, let's say it's like five, is multiplied by that percentage weight. Let's say it's like 50%. So that becomes 2.5. And then we add up the weights from all the networks that are included to get the final functional interaction weight. Exactly. The composite network. Okay. More questions. Okay. So now I'm going to explain together, explain to you how these weights that you see, they're computed. Okay. The reason that I'm going to explain this to you is you actually have some choices that you can make in the interface and it's important for me to explain why, like, how to make those choices. So then here's the idea, right? So you have three different types of networks in this case. You have to assign them all a weight, which is the reflection of, like, how much they're going to be used to generate the final network. How do you determine what those weights are? So the simplest possible thing that you can do is you can just assign them all equal weights. You can say, we don't know one way or another what's better, what network's better and what network network's not better. So the problem is, first of all, not every network is equally useful for every question. So if I've given you more information about what question you're asking by giving you a gene list, obviously you should up weight the networks that are most relevant to that gene list. The other thing, and so here on this slide I've explained that by saying that the gene function could be a whole bunch of different things. The other thing that can happen is, right, so the other thing that can happen, that's not only a question of relevance to the question, but some networks end up being redundant. And so what does that mean? Well, often what happens, especially with microarray data, is there's different labs doing the same assay at the same time. So if you get like 10 data sets that are all querying one aspect of function for a set of genes, you don't want to assign them all the same weight as a different data set that queries a completely different function of genes, because then all your queries are going to be focused on that specific function. So if you just assign equal weight to every network, you're not taking advantage of your sensitive to that. And so we have two rules for network weighting. I mean we use equations, obviously, but the two general rules are the networks should be relevant to the question that you're asking. And it shouldn't be redundant with the other networks that are already there. And the way in which we assign those weights takes into consideration both those two things. We use linear regression, a specific type of linear regression to assign the weights, and it identifies when it assigns those weights those weights reflect both the relevance and the redundancy. And so again if you give us a long list, we can come up with query specific weights that reflect what networks are relevant answering the question. Now by default if you don't do anything Gene Mania selects between two different ways of doing the network weighting. One is query specific but it only uses that if your list is sufficiently long. I think it has to be five genes or six genes. The reason that is if the list is short we just don't have enough information and so we're going to make like bad calls. We still might make bad calls if it's like five or six genes, but as the gene gets longer you can become more certain that the query specific weights are reflective of what you need. The other thing is that the gene list is short but if you only give us one gene we weight networks so that based on their ability to recapitulate the co-annotation patterns in gene ontology. So by that what I mean is if the network tends to link together genes that are assigned the same function it gets a greater weight. And as long as that weight is not redundant with some other network. So essentially you could think of the way in which we are computing the network weights under this gene ontology based weighting is that we take all the gene sets in gene ontology we compute the query specific network weights for them and then we average them together. So that the weight just reflects overall predictiveness for biological process annotation. Does that make sense? So now by default we use biological process because that's what most people are asking but you can switch over to molecular function or you can switch over to sell your component. And then if for some reason you don't want to use any of these weighting techniques there's the last category which is equal weighting so you can equal weight by data type so you can say the weight that I'm going to assign to all the co-expression networks in total is going to be same as the weight that I assigned to all the co-complex networks so if there's like 10 co-expression networks and one co-complex network each of those 10 will get one tenth of the weight of the one co-complex network or we can just assign equal weights to all the networks. So those are your options in terms of network weighting if you're not happy with the automatic choice that we've made you can change that choice.  Okay and so let's say for example this is the network that we've come up with it's not a very interesting looking network but this is the network these are the genes that are on the query list so those are the genes that define the question that we're asking and by color I'm using the color to reflect how highly those genes are scored. Okay so there's two main ways of scoring nodes one is that for each of the nodes each of the genes you look at its neighbors and you set its score to be the average of the score of its neighbors so what does that do? Well that means that this gene which has one neighbor that's in the original list is going to score less than this gene which has two neighbors in the list right and then these genes get no score at all because they have no neighbors in the list right but what this this type of scoring doesn't reflect is that you know this group of three genes is not connected to anything in the list at all but these genes down here have indirect connections to things in the list and then under some circumstances you might even have two genes that aren't directly connected but have a whole lot of indirect connections right so by indirect connections I mean that there is a path of length two that goes between the two nodes so this gene here is indirectly connected to this gene it's indirectly connected to this gene by path of length two and it's indirectly connected to that gene okay so we want to reflect the fact that these two genes even though they're not direct neighbors they're in some sense closer in the network than these genes over there where there's no path from the genes in our query list and these types of things are important say like if you're defining protein complexes you don't there's a lot of false negatives in these functional interaction networks right so if you have say for example a protein complex which you'd hope is that everything in the complex is going to be connected to each other by direct physical interaction but often it's not but often they share a lot of the neighbors they interact with the same sets of genes and if you share a lot of neighbors that is often a better indicator that you share a function than a direct interaction between yourself and one neighbor does that make sense okay so the method that we use in order to score genes once we have a network is called label propagation and essentially what it does is it reflects the difference between these two things so you can think about it as the following you first compute the direct neighborhood you score the genes in that way and then you continue and then you continue re-averaging the genes so now you look at this gene you take the average of it neighbors it's going to be a bit smaller you look at this gene you take the average of this it's neighbors it's going to be a bit smaller none of these genes are going to be connected by any pass to these red genes here so they're never going to get any score at all you can think of it as heat flowing through a network with some like heat sinks that cause you to lose label in some way or you can think of it as paint running through a network okay you don't have to understand the algorithm itself the important thing to understand is that it can distinguish between situations where genes just are indirectly connected to the query list and those where there's no connections at all and it scores genes highly if they have a lot of indirect connections to the query list yes exactly yeah so the problem with a lot of these interaction networks that people define there's false positives these are interactions that aren't real to get detected there's few of those, they're not that bad but there is a big problem with false negatives interactions that should have been detected but weren't connected and so there might be another network that gets uploaded at a different date that actually has that direct connection or maybe that direct connection and never gets observed by anybody but there's been various people who have noticed that when you have these false negatives interactions that you should have detected but you didn't you can also often find signatures of them by the fact that the two things that should have been connected share a lot of their neighbors and that's what we're trying to take advantage of with this algorithm precisely do you have an example? I mean I can give you a citation can you email me after and then I'll just give you some citations yeah so I'm saying two things if there's genes that have more indirect connections score more highly than genes that have few indirect connections and the other thing that I'm saying is under some circumstances if you have a whole lot of indirect connections then you can score more highly than something that only has a single direct connection great there's some details here which I've already explained but might help you with your note taking right so the take home is that the label propagation that we use because it allows indirect links to query genes to impact the scores often what happens is it will pull up a whole cluster of nodes because clusters of nodes that have a lot of interconnections also have a lot of indirect connections with one another okay so here's a label propagation example so a network and before you run label propagation it looks like this after you run label propagation it looks like this you can see here that these genes that all have a lot of connections with one another what they do is they strengthen their interactions right so these are all kind of high but there's not very strong scores for the other genes so this gene here is in the original query list right and that's kind of important because there's a lot of genes in these networks that are called hub genes and hub genes a lot of them are very multifunctional p53 is a perfect example of this so it's linked to a lot of different functions so if you're linked to p53 you don't want to inherit all the functions that p53 has you're linked to p53 because you share some aspect of its function right so by also relying on these indirect connections you can distinguish between direct connections that occur simply because you're linked to one of these pleotropic genes that have a lot of functions and specific functions that genes that have a small number of functions have okay so gene mania is three things it's a large automatically updated collection of interactions and networks and that's what a lot of people use gene mania for is they use it to actually download the networks that connect their gene set together right so even if you don't want to use gene function prediction you can still use gene mania for that um it's a query algorithm that finds genes and networks that are functionally related to your gene list okay and it also has this interactive client-side network browser with extensive link-outs I love using the website because what I do in talks is people mention gene and I type in a gene mania and I try to guess what they're going to say the gene function is and I can go through and I can link through and see all the other genes that it interacts with it's fun okay and so where the data sources come from we collect data from a lot of different data sources right now we have almost 2,000 networks in the gene mania um website and in the plugin and so our co-expression data comes from gene expression omnibus we download all the gene expression data sets that come from a platform that we recognize and have a sufficient number of samples in them that we're confident that the co-expression values that we compute are relatively accurate we get genetic and physical interactions from biogrid so what IREF index does is people who either generate large sets of physical interactions or people who go through and curate physical interactions reported in papers they put their physical interactions into something called IREF which is an overall body to kind of control the annotation of physical interactions that they generate and we do the downloads about we try to do them about quarterly about once every three months but often we end up doing it about once every six months okay we also get predicted interactions so these are what are called enterologs so these are interactions that have been observed between orthologs in a different species we also look at shared protein domains and recently we've added attributes and these are compiled by Gary Bader's lab and we also have some organism specific databases that we use and we get our gene annotations from gene ontology we ignore electronic annotation because those are unreliable so one thing that I actually haven't talked about if we're missing a network that you want you can upload that network to our database and uploading the network is dead simple you just put it into an Excel spreadsheet and each row is the two genes that interact so say you have like a hundred interactions you have a hundred rows and each row you have the gene identifiers for the genes that interact if you want you add another column which is the interaction score if you don't have a score we assume that they're all the interaction strength is all the same for all the genes you got to output that Excel spreadsheet and then use that network in exactly the same way that you would use any of the networks that are already in our database these are things you can do on the website you can also do with the Cytoscape plugin and you have a lot more functionality in the Cytoscape plugin so you can upload series of networks you can make your own network databases for example okay one more thing I want to tell you about is gene identifiers so we try to recognize all the unique gene identifiers that we can here's a list of the ones that we get the problem of course is a lot of people call genes by names that are synonymous with other genes and it's terrible there's something called SMG which refers to two distinct genes in Drosophila, one is called smog and I can't remember what the other one is called there's a lot of situations where you're using an alias for a gene that's not unique and because we want to simplify the input gene process we don't ask questions to try to figure out what sense of the gene that you mean which is to identify cases where the alias is non-specific then under those circumstances you can go and try to look up another name for the gene that is unique but besides that we try to identify any identify that people use so you don't have to do this identifier mapping step okay and the other thing is we use the ensemble database which we mirror but again we update about once every three months every six months so it's possible that we're not going to recognize a gene that's in the ensemble database because it's a new gene and if that happens just email us to let us know about it and we'll try to do something for you okay so currently we have eight organisms which one is missing maybe we only have seven right now yeast alright and we have about 2,000 networks and we have the network browser we're going to look at the cytoscape plugin though I encourage you to fool around with the network browser as well the other thing that we have we have a lot of offline command line tools to either like build up network bases for another organism and people have certainly done that and though so currently once you build up those network databases for different organisms you can access that through the cytoscape plugin what we're trying to do now is we're trying to make it easier for you to set up those network databases and make your own gene mania instance for that organism alright but again that's we're waiting for funding on that and it would happen maybe in the next year or two years but what we also have done is that we set up instances for people that we've collaborated with on specific organisms so far we've set up instances that we're working on for like cricket we're setting up an instance with the Gary set up an instance with a group at York and I can't remember what organism it's some single cell you carry on which? yes yes and so there's that currently we're just about to release gene mania for E. coli which is surprisingly hard to set up and we also have tools what one of them is called query runner where you can evaluate how much your new network contributes to overall knowledge about functional interactions or you can make a series of gene function predictions for all for example all the go categories and you can do that kind of offline to run these things and then assess them through cross validation okay so our major competitors the string database they've actually been around for about seven years longer and string has much of the functionality that I've talked to you about already the functionality that they don't have is they don't have different ways of waiting networks and their focus is slightly different than our focus but one thing that's very good about string is they cover a whole lot of different organisms right so if you're not if the organism you're interested in isn't one of the special eight string can probably help you okay and so these are the types of networks that you get out from string I think I've did I think my query gene here might have been rad50 right you can see they have kind of a nice nice interface they actually now use the network display tool that we developed for gene mania or at least the the cytoscape web plug-in that we developed and then actually if you click through if there's a structure for the corresponding protein you can click through and see that structure they also so you can see the like ours the color of the links indicate the source of the evidence and they're much better at tracking where individual what individual evidence is for individual interactions so you click through those interactions in many cases you can click through through the specific paper that reported the interaction even if that paper only reported one interaction we can provide that to you if the paper reported a hundred or more interactions but for like single interactions by like that we're reported by a paper from 20 years ago we're not going to be able to help you with that you know they also give you the gene function predictive partners I don't think they use they do go enrichment yet but they continue to do it they have a much better representation of the text mining so they're much better at recovering proteins whose names have been co-mented in PubMed at abstracts so they're very good for looking up what's going on in the literature and the literature with individual genes the thing to know about string is their protein focused they're not focused on genes so much as they're focused on proteins so for example we're focused on genes so we include things like genetic interactions we're a lot more permissive about the type of interactions that we permit because we have a very flexible way of doing network waiting we can do a lot of things on the fly any type of functional interaction we can incorporate string is a little bit more specific about the type of functional interactions that they allow and that might give them maybe a little bit more I don't know and so what they do is they come up with eight pre-computed networks for each organism these networks are a combination of all the networks together we have a little bit more flexibility that you can turn off or turn on some networks of the database of thousands that you want to be included in the query or not so you have a little bit more precision there the last thing that's a little bit different is they use direct interactions to score notes so they don't do this label propagation that I've been talking about with the indirect connections but nonetheless there's no reason not to try the same query and try the same query and see what you get because the types of things that we report are often complementary so E. coli hopefully that's coming soon you can find it actually if you want to play around with it you just go to the beta site you go beta.gmail.org E. coli is actually up there right now so if you like E. coli you can fool around with it we don't include it has to do with the fact that I don't think they contain a lot of functional information yet and we want to incorporate more phenotype information so disease associations of genes and right now we rely on other people to do our orthology mapping between organisms and as time goes on we're going to start doing a lot of that orthology mapping ourselves so we can map orthologist co-expression relationships for example okay so these are the URLs


Alright, we are live. I'm here with 8% nation and I have the distinct pleasure of interviewing the Insurance soup gang man Michael McCormick Taylor Dobby. What is up fellas? What's going on, man? Got it. Thank you guys so much for doing this seriously I've been following as I'm sure a thousand other interest agents have as well the insurance soup or Half a decade at least and I'm telling you what what you guys are doing is incredible It's amazing. These guys are that they bring they bring as much value And I mean this as much value to the insurance industry as anyone else on planet earth Gentlemen, welcome on. Oh man. Now I got some big now. I got some big expectations to live up to geez No doubt that they have a they have a Facebook group called insurance soup that if you are not a part of I'm telling you what You need to go to request to be in the insurance soup group They they were just shy and they'll break it today. They say 22,000 Insurance agents in the insurance soup group. They are all about bringing value to the insurance industry Michael McCormick is up in Stony Brook, New York and Taylor Dobby is about 25 miles south of Dallas down in Texas Super good dudes for those that don't know they are keynote speakers at this year's a Percentation Insurance Wealth Conference, which gentlemen is in 22 days 12 hours 10 minutes and 45 seconds from now Which means we probably need to start working on a presentation of some kind exactly Excited man. Super excited. You put together. I Think it's going to be by far the greatest conference for the industry that they've ever seen So super excited, you know same same we've been following you and your work You know what you've been doing for a while as well And it's always funny, you know when we talk to someone on the social media world that we we haven't actually met in person How long we stalk each other before a relationship actually comes about? I remember the very first time that you popped up.  And how soon before we're either friends or competing with one another so excited that it went down the road that it went down and you know here we are and Nashville is going to be a hell of a time No doubt. Thank you so much for the kind words Nashville, Tennessee, Nissan Stadium This event is like I said October 26 and 27 not too far away. All right, so Michael and Taylor For whoever would like to start I'd love to hear your guys's quick background and story about each of you How you got in the industry and also after after we introduce the two of you also how soup has Became a giant Gigantic Bola soup at this point. Yeah, so I'll go first. I got out of the military in 2012 I was medically discharged with some back issues. I was in the Air Force. I worked on aircraft So I got out started working on corporate jets and North Dallas worked on Mark Cubans Ross, Pro Junior You know a lot of the very successful men and Dallas, you know got got to work on their toys I got to a point where I physically couldn't get out of bed anymore and my wife was at the time taking over her uncle's farmers agency So I started going up there hanging out started to really enjoy, you know Just helping when got licensed and then became a farmers agent myself We took over her uncle's agency, but at the same time I started my own his agency was up and running for 35 years So it really threw me right into the middle of it, you know Not only on the cells, but service service inside as well A lot of agents when they come into the industry that servicing kind of slowly starts to pick up as they grow their book You know, I was thrown right into the middle of it With the agency being, you know an older agency things were still done an older way So it didn't take me long to realize, you know in order to grow the agency, you know We had to start, you know marketing like it was, you know year 2015 2016 instead of like 1990 So that's kind of how Mike and I actually met was on a social media marketing group and Started really diving in and that was the beginning of our journey down this, you know this rabbit hole of marketing and it was in June 2015 we started creative concepts Which is our social media marketing course then December of 2015. We started insurance suit the Facebook group My luck might go into a little bit more about, you know, how we started and how it's got to this point though Nice. Thank you Taylor. Thank you for your service buddy. That's awesome. Appreciate it. Yeah So, you know my story as far as how I got into insurance and how we get to where we are today When I first got out of college, I was in the mortgage industry mortgage industry was at that point pretty much like the wild wild west It was it was hot. It was fast. It was ridiculous and good money and It was a lot of fun, especially for a young guy just getting out of college, you know 23-24 years old making six figures plus doing doing something that you're having a lot of fun with it was it was it was all great and Then 2007 ish started a creep up and the writing started to show up on the walls as to what was going on the industry and My my girlfriend now wife was kind of in my ear about how You know, she was nervous about my being in an industry that looked like it was gonna crumble and fall in a part in the whole nine and I wound up playing it small and doing something that I generally don't do in my career all that often and I started playing very very conservatively and I moved into the retail banks and I started off as a branch manager for Jake and Morgan Chase and part of being a branch manager for Chase Requires you to have your life health six and sixty three You know you aid with some of the retirement planning all that kind of stuff and you know after doing that for a couple years I got recruited into a brand new private banking program in Citibank for their affluent clients and I was doing some retirement planning and some You know some stuff in in the In in the wealth world For a couple of years, but I was really really unhappy. I didn't like what I was doing I wasn't having fun with it and my boss at the time Even though I was I was doing phenomenally was just always just right on top of me And it got me to the point where I said to myself, you know what I don't I don't want a boss I don't need a boss and I'm generally more productive and and successful and happy when I don't have anyone to answer to and I'm just Well, that's kind of you know, follow my own create creativity And so I started shopping myself around a little bit and I was getting recruited by pretty much every life Life insurance company under the Sun every every P&C company under the Sun but most of them were looking to bring me on as a As a soldier and I've always kind of viewed myself as a general I wasn't looking to get the trenches if I was going to do something of substance within the industry was going to be an Ownership level and and finally I got the I got a knock on the door not literally, but my email from from State Farm and I went down that I went down that path and after Presenting my business plan a number of times. I finally got the I finally got the agency I have scratch here on the island and part of my business plan was to You know pretty much make it rain homeowners insurance because I've got an army of loan officers behind my back from my previous life when I was in mortgages and Long story short, they love my business plan But then on the second day after I opened I got a phone call from my sales leader letting me know that here on Long Island The most about a homes per month and new agents allowed to write is for because they're looking to take a little bit of a break from You know from gaining a market share on the heels of Hurricane Irene Hurricane Sandy they take out that's like a pretty bad beating What's that wow yeah, yeah, so you know the way I always put it as I felt like I opened up a dump in Donuts and On the second day after after opening up that dump in Donuts. They were like oh by the way you can't sell coffee So I kind of felt like I had I had my Know the the the rug pulled out from under me if you will And I stuck with it for for two years I put together a little bit over a two million dollar book But the way that the way that State Farm has got things set up if you are not Producing at a high level with life insurance You're struggling to really maximize your commissions and maximize your bonus and all the life insurance that I was producing was transactional I wasn't really putting people in policies they needed for true protection I was doing it to you know offer additional discounts and Not having a book of homeowners trying to convince people that are 23 24 years old living in an apartment or living in their mom basement or You know someone in their 40s that you know kind of failed to launch You know convinced them they like insurance No, I did my numbers But ultimately what ended up happening is as those people would shop their their auto insurance The life insurance would fall off and if your life insurance didn't stick on the books for at least two years They began to penalize your production for the year that you're in and I started losing policies because quite frankly the stuff that I was doing Was all transactional wasn't legitimate use-based so So anyway, we butt heads on on that and they weren't thrilled with with some of the marketing I was doing and we're butting heads a little bit on that because they like to have a lot of control over things and And ultimately I just kind of got to a point where I had to say to myself all right am I going to continue to struggle to Run a business as a business owner like I'm like I convinced myself that I am but state farms treating me like I'm not or do I or do I make a switch and ultimately I made a switch I opened up independent and Within a couple of months of being independent Everything that we were doing on the soup side the CAC side and a couple other projects that we had going we're booming at such a level where I kind of had to make a decision and I had to decide do I want to move into more of a consultancy role in this industry or continue to To grind it out as an owner and I love the marketing side man. It's so much fun I love social media probably I probably love social media a little bit too much if you ask my wife should tell you I definitely do even though even though she likes the money that we make and And and here we are today, so yeah, that's awesome, but thank you for sharing your story too. That's really cool Well, I can tell you what your personality really comes through on social, so it's just hilarious this Just the interaction and the stuff I see now because because Facebook friends and following the group and all that y'all do some really cool Stuff very unique, you know, like you kind of talked about you guys naturally just think outside the box extremely creative What's kind of what's kind of the differences real quick between like what Taylor's great at and what Michael's great at Why this has been such a great Partnership, well, I can answer that Mike is the brain behind You know a lot of the marketing that we do a lot of the strategies. He's he's the wizard behind the scenes You know, I learned from Mike every time we get on we put, you know together a new marketing campaign I'm taking notes And that's we work really well together one we keep each other in check You know, there's days where we both have to be like alright man Like it's time to get stuff done, you know, we need to pick up and improve here here and here like this is what you need to do This is what I need to do But yeah, Mike's the I consider the wizard behind the scenes so Yeah, and you know and refer for everything that I do on the marketing side You know Taylor is a complete wiz when it comes to automation and help it on the the back-end side of the of the marketing efforts and quite frankly the majority of the Behind-the-scenes business stuff that you know call it call it Administrative stuff that we don't pass along to one of our employees You know Taylor's your heads all that kind of stuff and Great, which is great for me because I'm a bull in the china shop, but it comes to anything administrative So it really works out well, so But but yeah, and to Taylor's point, you know, I mean When you're when you're operating a number of businesses that are all kind of being driven by social media You are constantly interacting with people and you're interacting with people in a manner that Know where people behave in in ways where they wouldn't necessarily behave in real life so we deal with a lot of people that have But a keyboard muscles keyboard tomatoes that kind of thing and the one thing that the two of us are both very very good at is Keeping the other one calm when the other one's feeling a little bit heated, so we we balance each other out and I don't want to get all mushy and say he's the yin and my yang or anything like that But he is my he is my brother from another mother. I love that. I love that that is so cool Well, you can tell you guys get along fantastic. I mean, it's it's like you've known each other literally your entire life Doesn't sound like you have But it's been that's been you know that just watching you guys and the things that you do and you know give it away a given away a Crew every year for your group, you know, I mean you guys just do some really cool stuff So it's just it's just super and super impressive from a marketing guy More previous sales guy that had to learn marketing It's I'm super super impressed and I'm learning from you guys every day. So thank you for everything You got a man, okay So what was it about because we could all jump we could all we could all stay on here for hours and hours and blast But I know we everybody's got stuff to do What is it about? What was it about this conference? That wanted you guys to get involved You know, how'd you how'd you notice it? You know kind of kind of feel us in on what was it about about this because you guys are such a great fit for this yeah, one thing that intrigued me is Everything that Mike and I do we try to bring the absolute best to the industry We don't get behind any program or product or software unless we believe that it's the absolute best And trying to find someone that shares the same passion and mindset that we do is Sometimes very hard to do You know our industry I still feel is 20 years behind so when we found you and 8% nation and Sol which are putting together it's it's forward thinking and it's thinking way bigger than a lot of people think and And that's what we try to do every single day is you know if if we aim small You know, it's easy to hit But when you aim big you can still miss it and still achieve much greater You know a much greater goal than you originally planned and sharing that same vision that you do is what really attracted me to it Thank you, buddy. That's awesome. Yeah, you're just a kind of piggyback off that you know, we're I guess you'd say in the grand scheme of things. We're still relatively new to our role within the industry You know just kind of just kind of like you are You know and one of the things that we've kind of found is we've established ourselves and grown at the pace that we've grown at and all that kind of stuff With all the different conferences that are out there It seems like and I could be wrong, but you know normally my gut is is what I go with It seems like a lot of these conferences are The same people talking about the same topics Just in a different city in front of the exact same audience over and over and over again And you've got these little tight insular circles within the leadership community of our industry That quite frankly are a little bit tough to break into you know, I mean, you know You got a lot of alphas. You got a lot of people who Look at look at the new guy or look at the new kids in the block and say, you know They don't they don't have the credibility to be speaking or I'm sure it's heck not sharing a spotlight with with anyone because you know that's that that's just not what I do or whatever and You know one of the things that we really liked about when you're putting together here as opposed to a lot of the different conferences that that you can find out there is that it is Truly a different approach a different angle You got some different faces and different different names and you see in the typical in the typical circles and And what let's call a spade a spade man. You went all in and I got a real appreciation for anyone who goes all in I mean you Gotta you got a damn football stadium man You got you got Grant Cardone you got Ray Lewis I mean you you went all in At a level that most I think myself included I won't speak for Taylor myself included would be probably a little bit too nervous to do So, you know, we saw what you were putting together and all that know the opportunity was of no-brainer Thank you, buddy. Thank you, man.   I'm probably from an industry standpoint I'm super excited about what you guys are because I know that whatever you do. It's gonna be unique It's gonna be creative. It's gonna be a different approach, you know, it's gonna be the two of you What will agents learn From what you're going to I know you don't have everything down specifically yet, but just just from you guys What what what can they learn in general and what do you think will learn from from stage? Yeah, and uh kind of in a nutshell without going too deep is you know We're gonna we're gonna teach kind of the blueprint how we've grown the the largest most pure entrance group and industry Almost 22,000, you know members in the group every member that's in there is vetted one by one to make sure that they You know are either an agent producer CSR They hold a spot within the industry that is not on the corporate level not on the vendor level And it keeps the audience pure so to teach an agent how to do that within their community So that they can have that audience that starts to know them like them and trust them It's not a quick overnight, you know success but it's a long-term play that will feed you time and time and time again and We've spent I mean the last three years learning and like you said, you know We failed many times along the way one thing I've always said especially to our clients is even if you fall on your face You're falling forward. So you're constantly moving forward even if you fail So they really teach agents how to do and build a community like we have But I learned it from our mistakes along the way as well one of the you know, one of the Hot and sexy topics in our industry, especially on social media is the whole idea of Advertising on Facebook and if you know how to do it, you're able to generate those leads very very inexpensively They come in highly highly targeted and they're exclusive and the whole nine but one of the areas that I think that a lot of agents really kind of sleep on and neglect is The amount of opportunity that could be located right underneath their nose if they just know how to source it and You don't need to be a paid-edge wizard. I mean do our guys know how to generate leads Absolutely, I mean we've had guys that have generated, you know, not that long ago We had someone generate 1700 leads for 2100 bucks We've had people generate 1400 leads for 1100 bucks generally speaking the leads come in three to eight three to nine dollars a piece But you know that they come in less than that But the the bottom line is whenever you're generating leads off of cold traffic That no like and trust factor is never gonna be baked into that prospect. You've got a really, you know Be on your game.  You got a text. You got an email You got a voice drop you got a knock on their doors. You got to send the carrier pigeons You got to do everything under the Sun to really warm them up again to interact with you and the reality of organic social lead gen is You can very easily find three to five people every single day that are gonna let you put out their insurance that The no like and trust factor has already been getting established for months before that prior conversation that that current conversation and You know, while we you know, obviously we're huge advocates of generating leads and you know We teach it and our clients have a lot of success with it What what a lot of people ultimately wind up raving about is the fact that not only are they able to generate all these leads you know through the the education that we put out there, but They're able to hop on Facebook every single day and know that they're gonna speak to three four or five people that are going to directly let them quote their insurance And and someone that's allowing you to quote their insurance is significantly different than someone who submitted their information for a future conversation about their insurance so You know, what we're we're gonna bring as far as the conference is concerned is we're gonna we're gonna Really kind of roll back the curtain and show you exactly like Taylor mentioned how we grew suit how we went from a group with no intention of monetizing to creating a essentially a business out of thin air a number of businesses out of thin air and Teach exactly how You know, everyone that's going to be in the audience the the agents that are in attendance can do the same within their own Local communities and all that kind of stuff and we actually have a couple of our of our agents that are to be in attendance And one of them who acts who absolutely crushes it You know Troy Johnson will be in attendance and you know, we can if you can find him in the audience while we're speaking Maybe we'll point them out or whatever, but you know when when you're when you're organically Mining social media for opportunity those opportunities come to you at that Referral quality that lay down quality that you're hoping for that you seldom get So it's gonna be a lot of fun and it's gonna be a slightly different look at the idea of lead jen in In a traditional sense. Yeah, because that's what that's immediately what people think is okay I've you know what leads well, I need I don't need these but I don't have any You know, maybe I don't have any money, you know And so what's really cool about what you guys do what you teach in soup and courage and concepts and other things that you Do is how to and again you reference what you're going to talk about stage every insurance agent It's gonna take away. I'm not gonna give it away right now But they're gonna take away one specific nugget at least one that I know of and I don't even know everything that you're gonna be Speaking on but I already know one specific nugget that the two of you will be delivering from stage and these on stadium That will change an agent's business Forever and it's incredible. So I'm excited for you guys go to give that value from stage at least one stadium from there So tell agents if they're on the fence In your estimation, what is it about this type of event this type of conference? And why should agents other than the fact that they get to meet the two of you and hang out, you know Why should agents attend? I'm a gambling man. You put me in Vegas. I'll set up the tables all day And I would push the chips all in that this will be the most value-packed conference for the industry That has ever come about and that's I say that with confidence the lineup of speakers The the program that you've put together if you're on the fence For the price of this conference is a drop in the bucket for the value that you will walk away in the next goal That you'll take from hands down That was so much more prolific than anything I had. I was gonna say you're on the fence. I'll let you stroke my beard And and if that's a turnoff then then I'll let you not stroke my beard I was gonna go with that but In all seriousness In all seriousness, I would agree. This is a lineup that is just front to back worth worth catching You know, you're gonna walk away with highly Highly productive and easy to implement strategies not just from us but from everyone that's gonna be up there on stage And it's gonna it's gonna be the type of thing where you know You're gonna be able to turn around get back to your get back to your office and implement And I don't I don't know the in-depth Topics that everyone is speaking on or whatnot, but I can tell you right now That if you are an agent who is planning on being in this industry industry for the long term But you are new and you are financially struggling and you're trying to figure out how to market effectively Without that budget If you get to this conference, you will walk away with strategies that you will be able to implement immediately That will cost you and your agency literally nothing Wow my gosh, so I Mean maybe I shouldn't say literally nothing it will cost you the price of admission to the conference But as far as an ongoing marketing strategy We are going to sow the seeds for you to be able to harvest from this point forward until you're it until you're ready to retire Dude, I love that. Thank you both. I That was that was that wasn't planned that wasn't scripted I'm telling you what Taylor about had me in freaking tears or about jumping up and down ready to tackle Ray Lewis, man That was I do got a There we go, I may be the only guy on stage big enough to actually tackle him Dude, we're got to set that up there I've noticed one thing today. I've at least got to work on my beard, right? One thing and one thing and it's been a curse and a blessing is Mike and I since the very beginning since we were member one and member two and insurance suit is we've stayed true to who we are I Mean we're we're real. We don't change It's pushed a lot of people away but it's attracted a lot and built a very loyal following and That's that's one of the things we're going to talk about is you don't have to try to please everyone You know be be yourself be real raw and relevant and you know your audience will come to you so When when you're constantly blown in the wind and trying to change who you are to satisfy the people that you're talking to or Whatever, you're essentially a cone of vanilla vanilla ice cream Everyone's gonna like it. No one's gonna love you and very few people are gonna go into the store and With 31 flavors and pick you so That's good. That's good. You heard of your first real brawl and relevant. I love that dude And you know what that's that's what learning you getting to know you guys over the short amount of time we have I'm telling you what there isn't two more honest transparent ethical dig down Actual genuine good, dude. I mean, it's I've been so overly impressed. I'm serious. It's been incredible Appreciate it. You got it. You got it. All right. Any final words gents before we wrap this thing up If you haven't bought your ticket You got to do so Yeah, and you know if you're if you're on the sidelines and trying to figure out how to you know put the You know put the scratch together to come or whatever You know Cody's put together a number of great opportunities. Don't just you know In our private Facebook group last night, we had one of the gals that really wanted to go saying that she couldn't find a hotel for less than 289 a night You know, you've got you've got some deals on on your website right now for 8% nation You know, there there are promo codes out there right now super soup will get you a discount on on a ticket I know you've got some other people out there that have got their own little their own little deals right now For for the conference so there there are ways for you to get there right now at a very very affordable price and And heck, I mean if you're struggling to find an affordable place to stay or anything like that Check out Airbnb. I was on there a little bit earlier today trying to find some of our agents a place to stay and You can find places for less than a hundred bucks a night I mean, there's really no excuse to not be there If your interest is Pete so, you know, there's a hundred and one ways to do it Let's just get it done and let's uh, you know, let's let's ask some fun at this conference. I love it I love it. Thank you so much. Hey, if you want To meet the insurance soup the faces behind the names Michael McCork and Taylor Dobby You got a bit 8% nation Nissan Stadium in Nashville 22 days 11 hours is counting down every single second gentlemen Thank you for being on and looking forward to seeing you at a personation insurance wealth conference It's gonna be a blast. We'll go forward to it be a lot of fun. Thank you gentlemen. Appreciate you being on very much. Thank you


In commerce, supply chain management is CM the management of the flow of goods and services to involves the movement and storage of raw materials, of work in process inventory, and of finished goods from point of origin to point of consumption. Interconnected or interlinked networks, channels and node businesses combined in the provision of products and services required by end customers in a supply chain.3 Supply chain management has been defined for as the design, planning, execution, control and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronizing supply with demand and measuring performance globally.5 SCM practice draws heavily from the areas of industrial engineering, systems engineering, operations management, logistics, procurement, information technology, and marketing 6 and strives for an integrated approach.Citation needed marketing channels play an important role in supply chain management.6 Current research in supply chain management is concerned with topics related to sustainability and risk management 7 among others. Some suggest that the people dimension of SCM, ethical issues, internal integration, transparency slash visibility, and human capital slash talent management are topics that have, so far, been underrepresented on the research agenda.8 SCM is the first company in the United States to implement the SCM in the United States. The SCM is the first company in the United States to implement the SCM in the United States.