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Volumn 25, Issue 22, 2009, Pages 3028-3030

CellClassifier: Supervised learning of cellular phenotypes

Author keywords

[No Author keywords available]

Indexed keywords

F ACTIN;

EID: 70449419850     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btp524     Document Type: Article
Times cited : (79)

References (12)
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    • CellProfiler: Image analysis software for identifying and quantifying cell phenotypes
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    • Carpenter, A.E.1
  • 5
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    • An empirical comparison of supervised learning algorithms
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    • Caruana,R. and Niculescu-Mizil,A. (2006) An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd International Conference on Machine Learning. Vol. 148 of ACM International Conference Proceeding Series. Pittsburgh, Pennsylvania, pp. 161-168.
    • (2006) ACM International Conference Proceeding Series , vol.148 , pp. 161-168
    • Caruana, R.1    Niculescu-Mizil, A.2
  • 7
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    • Jones,T.R. (2008) CellProfiler analyst: Data exploration and analysis software for complex image-based screens. BMC Bioinformatics, 9 1.
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  • 8
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    • Pelkmans,L. et al. (2005) Genome-wide analysis of human kinases in clathrin- and caveolae/raft-mediated endocytosis. Nature, 436 78-86.
    • (2005) Nature , vol.436 , pp. 78-86
    • Pelkmans, L.1
  • 9
    • 47149099717 scopus 로고    scopus 로고
    • Lessons from genetics: Interpreting complex phenotypes in RNAi screens
    • Sacher,R. et al. (2008) Lessons from genetics: Interpreting complex phenotypes in RNAi screens. Curr. Opin. Cell. Biol., 4 483-489.
    • (2008) Curr. Opin. Cell. Biol , vol.4 , pp. 483-489
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  • 11
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    • Snijder,B. et al. (2009) Population context determines cell-to-cell variability in endocytosis and virus infection. Nature [Epub ahead of print, doi:10.1038/nature08282, August 26, 2009].
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    • Integrating high-content screening and ligand-target prediction to identify mechanism of action
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.