Improved K-mer Based Prediction of Protein-Protein Interactions With
  Chaos Game Representation, Deep Learning and Reduced Representation Bias
Ruth Veevers Dan MacLean
Protein-protein interactions drive many biological processes, including the
detection of phytopathogens by plants' R-Proteins and cell surface receptors.
Many machine learning studies have attempted to predict protein-protein
interactions but performance is highly dependent on training data; models have
been shown to accurately predict interactions when the proteins involved are
included in the training data, but achieve consistently poorer results when
applied to previously unseen proteins. In addition, models that are trained
using proteins that take part in multiple interactions can suffer from
representation bias, where predictions are driven not by learned biological
features but by learning of the structure of the interaction dataset.
  We present a method for extracting unique pairs from an interaction dataset,
generating non-redundant paired data for unbiased machine learning. After
applying the method to datasets containing _Arabidopsis thaliana_ and pathogen
effector interations, we developed a convolutional neural network model capable
of learning and predicting interactions from Chaos Game Representations of
proteins' coding genes.