Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling

Molecular Diversity

Volumn 14, Issue 4, 2010, Pages 789-802

  Tanabe, Kazutoshi ^a,b   Lučić, Bono ^c   Amić, Dragan ^d   Kurita, Takio ^a   Kaihara, Mikio ^e   Onodera, Natsuo ^b   Suzuki, Takahiro ^f  

^a NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

^b UNIVERSITY OF TSUKUBA   (Japan)

^c RUDER BO KOVI INSTITUTE   (Croatia)

^d JOSIP JURAJ STROSSMAYER UNIVERSITY OF OSIJEK   (Croatia)

^e ICHINOSEKI NATIONAL COLLEGE OF TECHNOLOGY   (Japan)

^f TOYO UNIVERSITY   (Japan)

Author keywords

Carcinogenicity prediction; Correlation coefficient; Cross validation (CV); Molecular descriptors; Quantitative structure activity relationship (QSAR); Substructure grouping; Support vector classification (SVC); Support vector machine (SVM)

Indexed keywords

CARCINOGEN;

ARTICLE; CARCINOGENICITY; CHEMICAL STRUCTURE; COMPUTER PROGRAM; CONTROLLED STUDY; DATA BASE; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE;

ARTIFICIAL INTELLIGENCE; CARCINOGENS; COMPUTER SIMULATION; EFFICIENCY; FORECASTING; INORGANIC CHEMICALS; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REPRODUCIBILITY OF RESULTS; TASK PERFORMANCE AND ANALYSIS; VALIDATION STUDIES AS TOPIC;

EID: 78650180018     PISSN: 13811991     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11030-010-9232-y     Document Type: Article

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