Selection of negative examples in learning gene regulatory networks

Michele Ceccarelli, Luigi Cerulo

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

7 Citations (Scopus)

Abstract

Supervised learning methods have been recently exploited to learn gene regulatory networks from gene expression data. They consist of building a binary classifier from feature vectors composed by expression levels of a set of known regulatory connections, available in public databases (eg. RegulonDB, TRRD, Transfac, IPA), and using such a classifier to predict new unknown connections. The input to a binary supervised classifier consists normally of positive and negative examples, but usually the only available information are a partial set of gene regulations, i.e. positive examples, and unlabeled data which could include both positive and negative examples. A fundamental challenge is the choice of negative examples from such unlabeled data to make the classifier able to learn from data. We exploit the known topology of a gene network to select such negative examples and show whether such an assumption benefits the performance of a classifier.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
Pages56-61
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2009
Externally publishedYes
Event2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 - Washington, DC, United States
Duration: 1 Nov 20094 Nov 2009

Other

Other2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
CountryUnited States
CityWashington, DC
Period1/11/094/11/09

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Keywords

  • Gene regulatory networks
  • Machine learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Ceccarelli, M., & Cerulo, L. (2009). Selection of negative examples in learning gene regulatory networks. In Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 (pp. 56-61). [5332137] https://doi.org/10.1109/BIBMW.2009.5332137