Inferring gene regulatory networks by combining supervised and unsupervised methods

Turki Turki, Jason T.L. Wang, Ibrahim Rajikhan

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

11 Scopus citations

Abstract

Supervised methods for inferring gene regulatory networks (GRNs) perform well with good training data. However, when training data is absent, these methods are not applicable. Unsupervised methods do not need training data but their accuracy is low. In this paper, we combine supervised and unsupervised methods to infer GRNs using time-series gene expression data. Specifically, we use results obtained from unsupervised methods to train supervised methods. Since the results contain noise, we develop a data cleaning algorithm to remove noise, hence improving the quality of the training data. These refined training data are then used to guide classifiers including support vector machines and deep learning tools to infer GRNs through link prediction. Experimental results on several data sets demonstrate the good performance of the classifiers and the effectiveness of our data cleaning algorithm.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages140-145
Number of pages6
ISBN (Electronic)9781509061662
DOIs
StatePublished - Jan 31 2017
Event15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 - Anaheim, United States
Duration: Dec 18 2016Dec 20 2016

Publication series

NameProceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016

Other

Other15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Country/TerritoryUnited States
CityAnaheim
Period12/18/1612/20/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

Keywords

  • Data mining
  • Machine lorning
  • Network inference
  • Systems biology

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