A learning framework to improve unsupervised gene network inference

Turki Turki, William Bassett, Jason T.L. Wang

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

3 Scopus citations


Network inference through link prediction is an important data mining problem that finds many applications in computational social science and biomedicine. For example, by predicting links, i.e., regulatory relationships, between genes to infer gene regulatory networks (GRNs), computational biologists gain a better understanding of the functional elements and regulatory circuits in cells. Unsupervised methods have been widely used to infer GRNs; however, these methods often create missing and spurious links. In this paper, we propose a learning framework to improve the unsupervised methods. Given a network constructed by an unsupervised method, the proposed framework employs a graph sparsification technique for network sampling and principal component analysis for feature selection to obtain better quality training data, which guides three classifiers to predict and clean the links of the given network. The three classifiers include neural networks, random forests and support vector machines. Experimental results on several datasets demonstrate the good performance of the proposed learning framework and the classifiers used in the framework.

Original languageEnglish (US)
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings
EditorsPetra Perner
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319419190
StatePublished - 2016
Event12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016 - New York, United States
Duration: Jul 16 2016Jul 21 2016

Publication series

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


Other12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016
Country/TerritoryUnited States
CityNew York

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Applications in biology and medicine
  • Feature selection
  • Graph mining
  • Network analysis


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