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Volumn 31, Issue 5, 2015, Pages 761-763

DANN: A deep learning approach for annotating the pathogenicity of genetic variants

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; AREA UNDER THE CURVE; ARTIFICIAL NEURAL NETWORK; COMPUTER GRAPHICS; GENETIC SELECTION; GENETIC VARIATION; GENETICS; HUMAN; HUMAN GENOME; MOLECULAR GENETICS; SUPPORT VECTOR MACHINE;

EID: 84928997067     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu703     Document Type: Article
Times cited : (745)

References (7)
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    • Baker, M. (2012) One-stop shop for disease genes. Nature, 491, 171.
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  • 2
    • 70450265503 scopus 로고    scopus 로고
    • Optimized cutting plane algorithm for large-scale risk minimization
    • Franc, V. and Sonnenburg, S. (2009) Optimized cutting plane algorithm for large-scale risk minimization. J. Mach. Learn. Res., 10, 2157-2192.
    • (2009) J. Mach. Learn. Res. , vol.10 , pp. 2157-2192
    • Franc, V.1    Sonnenburg, S.2
  • 3
    • 84872143942 scopus 로고    scopus 로고
    • Analysis of 6, 515 exomes reveals the recent origin of most human protein-coding variants
    • Fu, W. et al. (2013) Analysis of 6, 515 exomes reveals the recent origin of most human protein-coding variants. Nature, 493, 216-220.
    • (2013) Nature , vol.493 , pp. 216-220
    • Fu, W.1
  • 4
    • 84895858942 scopus 로고    scopus 로고
    • A general framework for estimating the relative pathogenicity of human genetic variants
    • Kircher, M. et al. (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet., 46, 310-315.
    • (2014) Nat. Genet. , vol.46 , pp. 310-315
    • Kircher, M.1
  • 5
    • 80555140075 scopus 로고    scopus 로고
    • Scikit-learn: Machine learning in Python
    • Pedregosa, F. et al. (2011) Scikit-learn: machine learning in Python. J. Mach. Learn. Res., 12, 2825-2830.
    • (2011) J. Mach. Learn. Res. , vol.12 , pp. 2825-2830
    • Pedregosa, F.1
  • 7
    • 84892623436 scopus 로고    scopus 로고
    • On the importance of initialization and momentum in deep learning
    • Sutskever, I. et al. (2013) On the importance of initialization and momentum in deep learning. In ICML-13, pp. 1139-1147.
    • (2013) ICML-13 , pp. 1139-1147
    • Sutskever, I.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.