Extracting compact representation of knowledge from gene expression data for protein-protein interaction

Haohan Wang, Aman Gupta, Ming Xu

Research output: Contribution to journalArticlepeer-review


DNA microarrays help measure the expression levels of thousands of genes concurrently. A major challenge is to extract biologically relevant information and knowledge from massive amounts of microarray data. In this paper, we explore learning a compact representation of gene expression profiles by using a multi-task neural network model, so that further analyses can be carried out more efficiently on the data. The proposed network is trained with prediction tasks for Protein-Protein Interactions (PPIs), predicting Gene Ontology (GO) similarities as well as geometrical constrains, while simultaneously learning a high-level representation of gene expression data. We argue that deep networks can extract more information from expression data as compared to standard statistical models. We tested the utility of our method by comparing its performance with famous feature extraction and dimensionality reduction methods on the task of PPI prediction, and found the results to be promising.

Original languageEnglish (US)
Pages (from-to)279-292
Number of pages14
JournalInternational Journal of Data Mining and Bioinformatics
Issue number4
StatePublished - 2017
Externally publishedYes


  • Computational biology
  • Convolutional neural network
  • Deep learning
  • Feature extraction
  • Gene expression
  • Knowledge representation
  • Multi-task network
  • PPI prediction

ASJC Scopus subject areas

  • Information Systems
  • General Biochemistry, Genetics and Molecular Biology
  • Library and Information Sciences


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