Application of hidden Markov models to gene prediction in DNA

Michael M. Yin, Jason T.L. Wang

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

8 Scopus citations

Abstract

Programs currently available for gene prediction from within genomic DNA are far from being powerful enough to elucidate the gene structure completely. We develop a hidden Markov model (HMM) to represent the degeneracy features of splicing junction donor sites in eucaryotic genes. The HMM system is fully trained using an expectation maximization algorithm and the system performance is evaluated using the 10-way cross-validation method. Experimental results show that our HMM system can correctly classify more than 95% of the candidate sequences into the right categories. More than 91% of the true donor sites and 97% of the false donor sites in the test data are classified correctly. These results are very promising, considering that only the local information in DNA is used. This model will be a very important component of effective and accurate gene structure detection system currently being developed in our lab.

Original languageEnglish (US)
Title of host publicationProceedings - 1999 International Conference on Information Intelligence and Systems, ICIIS 1999
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages40-47
Number of pages8
ISBN (Electronic)0769504469, 9780769504469
DOIs
StatePublished - Jan 1 1999
Event1999 International Conference on Information Intelligence and Systems, ICIIS 1999 - Bethesda, United States
Duration: Oct 31 1999Nov 3 1999

Publication series

NameProceedings - 1999 International Conference on Information Intelligence and Systems, ICIIS 1999

Other

Other1999 International Conference on Information Intelligence and Systems, ICIIS 1999
CountryUnited States
CityBethesda
Period10/31/9911/3/99

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Application of hidden Markov models to gene prediction in DNA'. Together they form a unique fingerprint.

Cite this