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Volumn 31, Issue 15, 2015, Pages 2595-2597

PRROC: Computing and visualizing Precision-recall and receiver operating characteristic curves in R

Author keywords

[No Author keywords available]

Indexed keywords

AREA UNDER THE CURVE; AUTOMATED PATTERN RECOGNITION; COMPUTER GRAPHICS; COMPUTER INTERFACE; HUMAN; MATHEMATICAL COMPUTING; RECEIVER OPERATING CHARACTERISTIC; SOFTWARE; STATISTICAL ANALYSIS;

EID: 84943599060     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv153     Document Type: Article
Times cited : (283)

References (8)
  • 1
    • 84886486483 scopus 로고    scopus 로고
    • Area under the precision-recall curve: Point estimates and confidence intervals
    • Blockeel, H., Kersting, K., Nijssen, S., and? Zelezný, F. ed., Springer, Berlin
    • Boyd, K. et al. (2013) Area under the precision-recall curve: point estimates and confidence intervals. In: Blockeel, H., Kersting, K., Nijssen, S., and? Zelezný, F. (ed.) Machine Learning and Knowledge Discovery in Databases. Vol. 8190 of LNCS. Springer, Berlin, pp. 451-466.
    • (2013) Machine Learning and Knowledge Discovery in Databases. Vol. 8190 of LNCS , pp. 451-466
    • Boyd, K.1
  • 4
    • 84890024582 scopus 로고    scopus 로고
    • A general approach for discriminative de novo motif discovery from high-throughput data
    • Grau, J. et al. (2013) A general approach for discriminative de novo motif discovery from high-throughput data. Nucleic Acids Res., 41, e197.
    • (2013) Nucleic Acids Res. , vol.41 , pp. e197
    • Grau, J.1
  • 5
    • 84925136125 scopus 로고    scopus 로고
    • Area under precision-recall curves for weighted and unweighted data
    • Keilwagen, J. et al. (2014) Area under precision-recall curves for weighted and unweighted data. PLoS One, 9, e92209.
    • (2014) PLoS One , vol.9 , pp. e92209
    • Keilwagen, J.1
  • 6
    • 84912571936 scopus 로고    scopus 로고
    • Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty
    • Mihaljevic, B. et al. (2014) Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty. Front. Comput. Neurosci., 8, 150.
    • (2014) Front. Comput. Neurosci. , vol.8 , pp. 150
    • Mihaljevic, B.1
  • 7
    • 79952709519 scopus 로고    scopus 로고
    • PROC: An open-source package for R and S+ to analyze and compare ROC curves
    • Robin, X. et al. (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77.
    • (2011) BMC Bioinformatics , vol.12 , pp. 77
    • Robin, X.1
  • 8
    • 27544491192 scopus 로고    scopus 로고
    • ROCR: Visualizing classifier performance in R
    • Sing, T. et al. (2005) ROCR: visualizing classifier performance in R. Bioinformatics, 21, 3940-3941.
    • (2005) Bioinformatics , vol.21 , pp. 3940-3941
    • Sing, T.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.