Reverse engineering gene regulatory networks using sampling and boosting techniques

Turki Turki, Jason T.L. Wang

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

4 Scopus citations

Abstract

Reverse engineering gene regulatory networks (GRNs), also known as network inference, refers to the process of reconstructing GRNs from gene expression data. Biologists model a GRN as a directed graph in which nodes represent genes and links show regulatory relationships between the genes. By predicting the links to infer a GRN, biologists can gain a better understanding of regulatory circuits and functional elements in cells. Existing supervised GRN inference methods work by building a feature-based classifier from gene expression data and using the classifier to predict the links in GRNs. Observing that GRNs are sparse graphs with few links between nodes, we propose here to use under-sampling, over-sampling and boosting techniques to enhance the prediction performance. Experimental results on different datasets demonstrate the good performance of the proposed approach and its superiority over the existing methods.

Original languageEnglish (US)
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 13th International Conference, MLDM 2017, Proceedings
EditorsPetra Perner
PublisherSpringer Verlag
Pages63-77
Number of pages15
ISBN (Print)9783319624150
DOIs
StatePublished - 2017
Event13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017 - New York, United States
Duration: Jul 15 2017Jul 20 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10358 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017
Country/TerritoryUnited States
CityNew York
Period7/15/177/20/17

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Applications in biology and medicine
  • Boosting techniques
  • Graph mining
  • Sampling methods
  • Supervised learning

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