Learning functional embedding of genes governed by pair-wised labels

In this work, we build a deep neural network architecture which learns a compact numerical representation of genes supervised by numerous sources of pair-wise information, including Protein-Protein Interaction information and Gene Ontology information. We introduce a new network architecture which can process gene expression data and generate the representation of individual genes while governed by pair-wise information. The learnt representation is aimed to be further used for research of bioinformatics on relevant tasks, and even beyond the information sources from embedding learnt. Within this paper, we evaluate the representation on Protein-Protein Interaction task, and it shows a result which is better than learnt representation from traditional dimension reduction and feature selection methods.