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Volumn 20, Issue SUPPL. 1, 2004, Pages

Splice site identification by idlBNs

Author keywords

[No Author keywords available]

Indexed keywords

BAYES THEOREM; CONFERENCE PAPER; DNA BINDING; EXON; GENE IDENTIFICATION; GENETIC ANALYSIS; HUMAN; MOUSE; NONHUMAN; NUCLEOTIDE SEQUENCE; PRIORITY JOURNAL; RAT; RNA SPLICING; SAMPLE SIZE;

EID: 2942520939     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/bth932     Document Type: Conference Paper
Times cited : (36)

References (20)
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  • 5
    • 0034136782 scopus 로고    scopus 로고
    • Modeling splice sites with Bayes networks
    • Cai, D., Delcher, A., Kao, B. and Kasif, S. (2000) Modeling splice sites with Bayes networks. Bioinformatics, 16, 152-158.
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    • Cai, D.1    Delcher, A.2    Kao, B.3    Kasif, S.4
  • 6
    • 2542465947 scopus 로고    scopus 로고
    • On inclusion-driven learning of Bayesian networks
    • Castelo, R. and Kočka, T. (2003) On inclusion-driven learning of Bayesian networks. J. Mach. Learn. Res., 4: 527-574.
    • (2003) J. Mach. Learn. Res. , vol.4 , pp. 527-574
    • Castelo, R.1    Kočka, T.2
  • 7
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    • A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations
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    • Chernoff, H.1
  • 8
    • 0042967741 scopus 로고    scopus 로고
    • Optimal structure identification with greedy search
    • Chickering, D. (2002) Optimal structure identification with greedy search. J. Mach. Learn. Res., 3, 507-554.
    • (2002) J. Mach. Learn. Res. , vol.3 , pp. 507-554
    • Chickering, D.1
  • 10
    • 17444368519 scopus 로고    scopus 로고
    • Modeling DNA splice regions by learning Bayesian networks
    • Center for Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA
    • Dash, D. and Gopalakrishnan, V. (2001) Modeling DNA splice regions by learning Bayesian networks. Technical report, Center for Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA.
    • (2001) Technical Report
    • Dash, D.1    Gopalakrishnan, V.2
  • 12
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • Heckerman, D., Geiger, D. and Chickering, D. (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach. Learn., 20, 194-243.
    • (1995) Mach. Learn. , vol.20 , pp. 194-243
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  • 14
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    • On information and sufficiency
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    • Neyman, J.1    Pearson, E.2
  • 18
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    • Evaluation of gene-finding programs on mammalian sequences
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  • 20
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    • Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals
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