Combinatorial method of splice sites prediction

Alexander Churbanov, Hesham Ali

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

3 Scopus citations

Abstract

Predicting and proper ranking of splice sites (SS) is a challenging problem in bioinformatics and machine learning communities. Proposed method of donor and acceptor SSs prediction is based on counting oligonucleotide frequencies for splice and splice-like signals. Based on bayesian principle SS sensors were built. We demonstrate advantage of our proposed sensor design compared with existing sensors and tools. In particular, our donor sensor outperforms Maximum Entropy Sensor for several representative test sets of genes when compared on Receiver Operating Characteristic (ROC) curve. We represent combinatorial interaction of SSs and related factors with Logarithm Of oDds (LOD) weight matrices. Based on factor interactions we were able to substantially improve splice signals prediction quality and rank SSs better than SpliceView, GeneSplicer, NNSplice and Genio tools. Proposed method is used in our new splicing simulator SpliceScan.

Original languageEnglish (US)
Title of host publication2005 IEEE Computational Systems Bioinformatics Conference, Workshops and Poster Abstracts
Pages189-190
Number of pages2
DOIs
StatePublished - 2005
Event2005 IEEE Computational Systems Bioinformatics Conference, Workshops and Poster Abstracts - Stanford, CA, United States
Duration: Aug 8 2005Aug 11 2005

Publication series

Name2005 IEEE Computational Systems Bioinformatics Conference, Workshops and Poster Abstracts

Conference

Conference2005 IEEE Computational Systems Bioinformatics Conference, Workshops and Poster Abstracts
Country/TerritoryUnited States
CityStanford, CA
Period8/8/058/11/05

ASJC Scopus subject areas

  • Engineering(all)

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