Learning functional embedding of genes governed by pair-wised labels

Jingjun Cao, Zhengli Wu, Wenting Ye, Haohan Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish (US)
Title of host publication2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017
Number of pages5
ISBN (Electronic)9781538620304
StatePublished - Dec 4 2017
Externally publishedYes


  • Computational Biology
  • Deep Learning
  • Representation Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications


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