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Volumn 2373, Issue , 2002, Pages 190-202

A better method for length distribution modeling in HMMS and its application to gene finding

Author keywords

Computational biology; Gene finding; Hidden Markov models; Length distribution

Indexed keywords

BIOINFORMATICS; GENES; GEOMETRY; PATTERN MATCHING; TOPOLOGY;

EID: 29144511447     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45452-7_17     Document Type: Conference Paper
Times cited : (3)

References (10)
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  • 2
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    • Burset, M.1    Guigó, R.2
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    • The DNA sequence of human chromosome 22
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    • Dunham, I.1
  • 8
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    • A computational analysis of sequence features involved in recognition of short introns
    • L. P. Lim and C. B. Burge. A computational analysis of sequence features involved in recognition of short introns. Proceedings of the National Academy of Sciences USA, 98(20):11193–11198, 2001.
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    • Lim, L.P.1    Burge, C.B.2
  • 9
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    • Technical Report ALCOMFT-TR-01-144, Algorithms and Complexity – Future Technologies Project (ALCOM-FT), June
    • R. B. Lyngsø and C. N. S. Pedersen. Complexity of comparing hidden Markov models. Technical Report ALCOMFT-TR-01-144, Algorithms and Complexity – Future Technologies Project (ALCOM-FT), June 2001
    • (2001) Complexity of Comparing Hidden Markov Models
    • Lyngsø, R.B.1    Pedersen, C.N.2
  • 10
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    • A tutorial on Hidden Markov models and selected applications in speech recognition
    • L. R. Rabiner. A tutorial on Hidden Markov models and selected applications in speech recognition. P roceedings of the IEEE, 77(2):257–285, 1989.
    • (1989) P Roceedings of the IEEE , vol.77 , Issue.2 , pp. 257-285
    • Rabiner, L.R.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.