Recognizing Splicing Junction Acceptors in Eukaryotic Genes Using Hidden Markov Models and Machine Learning Methods

Michael M. Yin, Jason T.L. Wang

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

3 Scopus citations

Abstract

The development of the hidden Markov model (HMM) acceptor model for splicing junction acceptor sites recognition was discussed. An HMM with 16 states and a set of transitions was defined for modeling a true acceptor site. The states and transitions were represented as a digraph where states corresponded to vertices and transitions to edges. Each state was associated with a discrete output probability distribution. The performance evaluation of the HMM system for true acceptor sites showed that on average, the system correctly detected 91.9% of the true acceptor sites in the test data.

Original languageEnglish (US)
Title of host publicationProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 2
EditorsP.P. Wang, P.P. Wang
Pages786-789
Number of pages4
Edition2
StatePublished - 2000
EventProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000 - Atlantic City, NJ, United States
Duration: Feb 27 2000Mar 3 2000

Publication series

NameProceedings of the Joint Conference on Information Sciences
Number2
Volume5

Other

OtherProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000
Country/TerritoryUnited States
CityAtlantic City, NJ
Period2/27/003/3/00

All Science Journal Classification (ASJC) codes

  • General Computer Science

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