Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.
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CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.On the information expressed in enzyme structure: more lessons from ribonuclease A.Which is a more accurate predictor in colorectal survival analysis? Nine data mining algorithms vs. the TNM staging systemA clinical risk stratification tool for predicting treatment resistance in major depressive disorderHigh-Dimensional descriptor selection and computational QSAR modeling for antitumor activity of ARC-111 analogues Based on Support Vector Regression (SVR).In Silico Estimation of Chemical Carcinogenicity with Binary and Ternary Classification Methods.
P2860
Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.
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2010 nî lūn-bûn
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2010年の論文
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2010年学术文章
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2010年学术文章
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2010年学术文章
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2010年学术文章
@zh-my
2010年学术文章
@zh-sg
2010年學術文章
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2010年學術文章
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2010年學術文章
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name
Prediction of carcinogenicity ...... ure grouping and SVM modeling.
@en
Prediction of carcinogenicity ...... ure grouping and SVM modeling.
@nl
type
label
Prediction of carcinogenicity ...... ure grouping and SVM modeling.
@en
Prediction of carcinogenicity ...... ure grouping and SVM modeling.
@nl
prefLabel
Prediction of carcinogenicity ...... ure grouping and SVM modeling.
@en
Prediction of carcinogenicity ...... ure grouping and SVM modeling.
@nl
P2093
P2860
P1433
P1476
Prediction of carcinogenicity ...... ture grouping and SVM modeling
@en
P2093
Bono Lučić
Dragan Amić
Kazutoshi Tanabe
Mikio Kaihara
Natsuo Onodera
Takahiro Suzuki
P2860
P2888
P304
P356
10.1007/S11030-010-9232-Y
P50
P577
2010-02-26T00:00:00Z