Effective hidden Markov models for detecting splicing junction sites in DNA sequences

Information Sciences

Volumn 139, Issue 1-2, 2001, Pages 139-163

  Yin, Michael M ^a   Wang, Jason T L ^a  

^a NEW JERSEY INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Bioinformatics; Computational biology; Gene finding; Hidden Markov models; Splicing junction

Indexed keywords

ALGORITHMS; BIOCOMMUNICATIONS; GENES; MARKOV PROCESSES; MATHEMATICAL MODELS;

HIDDEN MARKOV MODELS (HMM); SPLICING JUNCTION SITES;

DNA;

EID: 0035504952     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0020-0255(01)00160-8     Document Type: Conference Paper

References (16)

1. Detection of eukaryotic promoters using Markov transition matrices
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6. Computational gene identification: An open problem
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8. GeneMark.hmm: New solutions for gene finding
9. A method for identifying splice sites and translational start sites in eukaryotic mRNA
10. New techniques for DNA sequence classification
11. Algorithms for splicing junction donor recognition in genomic DNA sequences
12. Application of hidden Markov models to gene prediction in DNA
13. Recognizing splicing junction acceptors in eukaryotic genes using hidden Markov models and machine learning methods
14. Application of hidden Markov models to biological data mining: A case study