Classification of gene expression levels using activator and repressor motifs

Huitao Sheng, Kishan Mehrotra, Chilukuri K Mohan, Ramesh Raina

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

Abstract

Gene expression levels are influenced significantly by the presence or absence of cis-regulatory elements or motifs. This paper presents classification systems in which the occurrences of both activator and repressor motifs constitute important inputs in predicting whether a gene will be upregulated, down-regulated, or neither (neutral). We have experimented with several approaches for classification using these input data, and best performance was obtained using Support Vector Machine (SVM) models with linear kernels and a hierarchical structure. On Saccharomyces cerevisiae data, this approach yielded 71% accuracy (on test data) for 3-category classification.

Original languageEnglish (US)
Title of host publicationProceedings - 2008 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW
Pages215-218
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW - Philadelphia, PA, United States
Duration: Nov 3 2008Nov 5 2008

Other

Other2008 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW
CountryUnited States
CityPhiladelphia, PA
Period11/3/0811/5/08

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ASJC Scopus subject areas

  • Molecular Biology
  • Information Systems
  • Biomedical Engineering

Cite this

Sheng, H., Mehrotra, K., Mohan, C. K., & Raina, R. (2008). Classification of gene expression levels using activator and repressor motifs. In Proceedings - 2008 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW (pp. 215-218). [4686239] https://doi.org/10.1109/BIBMW.2008.4686239