A new approach to link prediction in gene regulatory networks

Turki Turki, Jason Wang

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations

Abstract

Link prediction is an important data mining problem that has many applications in different domains such as social network analysis and computational biology. For example, biologists model gene regulatory networks (GRNs) as directed graphs where nodes are genes and links show regulatory relationships between the genes. By predicting links in GRNs, biologists can gain a better understanding of the cell regulatory circuits and functional elements. Existing supervised methods for GRN inference work by building a feature-based classifier from gene expression data and using the classifier to predict links in the GRNs. In this paper we present a new supervised approach for link prediction in GRNs. Our approach employs both gene expression data and topological features extracted from the GRNs, in combination with three machine learning algorithms including random forests, support vector machines and neural networks. Experimental results on different datasets demonstrate the good performance of the proposed approach and its superiority over the existing methods.

Original languageEnglish (US)
Article numberA47
Pages (from-to)404-415
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9375 LNCS
DOIs
StatePublished - Jan 1 2015
Event16th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2015 - Wroclaw, Poland
Duration: Oct 14 2015Oct 16 2015

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Bioinformatics
  • Data mining
  • Feature selection
  • Machine learning
  • Systems biology

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