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Volumn 26, Issue 2, 2010, Pages 280-282

Vibe 2.0: Visual integration for Bayesian evaluation

Author keywords

[No Author keywords available]

Indexed keywords

PROTEOME;

EID: 77950487416     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btp639     Document Type: Article
Times cited : (11)

References (10)
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    • Estimating the posterior probabilities using the k-nearest neighbor rule.
    • Atiya, A.F. (2005). "Estimating the posterior probabilities using the k-nearest neighbor rule." Neural Comput., 17, 731-740.
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    • Atiya, A.F.1
  • 2
    • 28444475160 scopus 로고    scopus 로고
    • A data integration methodology for systems biology
    • Hwang, D. et al. (2005) A data integration methodology for systems biology. Proc. Natl Acad. Sci. USA. 102, 17296-17301.
    • (2005) Proc. Natl Acad. Sci. USA. , vol.102 , pp. 17296-17301
    • Hwang, D.1
  • 3
    • 0034653589 scopus 로고    scopus 로고
    • An algorithm for automated bacterial identification using matrix-assisted laser desorption/ionization mass spectrometry
    • Jarman, K.H. et al. (2000) An algorithm for automated bacterial identification using matrix-assisted laser desorption/ionization mass spectrometry. Anal. Chem., 72, 1217-1223.
    • (2000) Anal. Chem. , vol.72 , pp. 1217-1223
    • Jarman, K.H.1
  • 4
    • 44949264443 scopus 로고    scopus 로고
    • Bayesian-integrated microbial forensics
    • Jarman, K.H. et al. (2008) Bayesian-integrated microbial forensics. Appl. Environ. Microbiol., 74, 3573-3582.
    • (2008) Appl. Environ. Microbiol. , vol.74 , pp. 3573-3582
    • Jarman, K.H.1
  • 5
    • 8844263749 scopus 로고    scopus 로고
    • A statistical framework for genomic data fusion
    • Lanckriet, G.R. et al. (2004) A statistical framework for genomic data fusion. Bioinformatics, 20, 2626-2635.
    • (2004) Bioinformatics , vol.20 , pp. 2626-2635
    • Lanckriet, G.R.1
  • 6
    • 22244447463 scopus 로고    scopus 로고
    • Assessing the limits of genomic data integration for predicting protein networks
    • Lu, L.J. et al. (2005) Assessing the limits of genomic data integration for predicting protein networks. Genome Res., 15, 945-953.
    • (2005) Genome Res. , vol.15 , pp. 945-953
    • Lu, L.J.1
  • 8
    • 0004255908 scopus 로고    scopus 로고
    • McGraw Hill Higher Education, Columbus
    • Mitchell, T. (1997). Machine Learning. McGraw Hill Higher Education, Columbus.
    • (1997) Machine Learning
    • Mitchell, T.1
  • 9
    • 0038492417 scopus 로고    scopus 로고
    • A Bayesian framework for combining heterogeneous data sources for gene function prediction
    • Troyanskaya, O.G. et al. (2003) A Bayesian framework for combining heterogeneous data sources for gene function prediction. Proc. Natl Acad. Sci. USA, 100, 8348-8353.
    • (2003) Proc. Natl Acad. Sci. USA , vol.100 , pp. 8348-8353
    • Troyanskaya, O.G.1
  • 10
    • 61949377666 scopus 로고    scopus 로고
    • A Bayesian integration model of high-throughput proteomics and metabolomics data for improved early detection of microbial infections
    • Webb-Robertson, B.-J. et al. (2009) A Bayesian integration model of high-throughput proteomics and metabolomics data for improved early detection of microbial infections. Pac. Symp. Biocomput., 14, 451-463.
    • (2009) Pac. Symp. Biocomput. , vol.14 , pp. 451-463
    • Webb-Robertson, B.-J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.