Predicting Protein Function via Semantic Integration of Multiple Networks

Guoxian Yu, Guangyuan Fu, Jun Wang, Hailong ZHU

Research output: Contribution to journalJournal articlepeer-review

38 Citations (Scopus)


Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically i ntegrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at

Original languageEnglish
Article number7164278
Pages (from-to)220-232
Number of pages13
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number2
Publication statusPublished - 1 Mar 2016

Scopus Subject Areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

User-Defined Keywords

  • Function prediction
  • multiple networks
  • network-based classifier
  • semantic similarity


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