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

Michael M. Yin, Jason 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 - Dec 1 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
CountryUnited States
CityAtlantic City, NJ
Period2/27/003/3/00

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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