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Feature Selection for the Promoter Recognition and Prediction Problem

Feature Selection for the Promoter Recognition and Prediction Problem

George Potamias, Alexandros Kanterakis
Copyright: © 2007 |Volume: 3 |Issue: 3 |Pages: 19
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781615202072|EISSN: 1548-3924|DOI: 10.4018/jdwm.2007070105
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MLA

Potamias, George, and Alexandros Kanterakis. "Feature Selection for the Promoter Recognition and Prediction Problem." IJDWM vol.3, no.3 2007: pp.60-78. http://doi.org/10.4018/jdwm.2007070105

APA

Potamias, G. & Kanterakis, A. (2007). Feature Selection for the Promoter Recognition and Prediction Problem. International Journal of Data Warehousing and Mining (IJDWM), 3(3), 60-78. http://doi.org/10.4018/jdwm.2007070105

Chicago

Potamias, George, and Alexandros Kanterakis. "Feature Selection for the Promoter Recognition and Prediction Problem," International Journal of Data Warehousing and Mining (IJDWM) 3, no.3: 60-78. http://doi.org/10.4018/jdwm.2007070105

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Abstract

With the completion of various whole genomes, one of the fundamental bioinformatics tasks is the identification of functional regulatory regions, such as promoters, and the computational discovery of genes from the produced DNA sequences. Confronted with huge amounts of DNA sequences, the utilization of automated computational sequence analysis methods and tools is more than demanding. In this article, we present an efficient feature selection to the promoter recognition, prediction, and localization problem. The whole approach is implemented in a system called MineProm. The basic idea underlying our approach is that each position-nucleotide pair in a DNA sequence is represented by a distinct binary-valued feature—the binary position base value (BPBV). A hybrid filter-wrapper, featuredeletion (or addition) algorithmic process is called for in order to select those BPBVs that best discriminate between two DNA sequences target classes (i.e., promoter vs. nonpromoter). MineProm is tested on two widely used benchmark data sets. Assessment of results demonstrates the reliability of the approach.

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