Predicting the androgenicity of structurally diverse compounds from molecular structure using different classifiers

QSAR and Combinatorial Science

Volumn 28, Issue 5, 2009, Pages 542-550

  Ji, Li ^a   Wang, Xiaodong ^a   Qin, Liang ^a   Luo, Si ^a   Wang, Liansheng ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

Androgenicity; Binary classification; Consensus model; Learning vector quantization; Probabilistic neural network; Support vector machine

Indexed keywords

CLASSIFICATION (OF INFORMATION); FORECASTING; INDUSTRIAL CHEMICALS; MOLECULAR STRUCTURE; SUPPORT VECTOR MACHINES; VECTOR QUANTIZATION; VECTORS;

ANDROGENICITY; BINARY CLASSIFICATION; CLASSIFICATION MODELS; CONSENSUS MODELS; CORRELATION COEFFICIENT; LEARNING VECTOR QUANTIZATION; NEURAL-NETWORKS; PROBABILISTIC NEURAL NETWORK; PROBABILISTICS; SUPPORT VECTORS MACHINE;

NEURAL NETWORKS;

4 OCTYLPHENOL; AMARANTH; ANDROGEN; ANTHRAFLAVIC ACID; CHLORPHENOTANE; CHRYSENE; CLOMIFENE; ENDOCRINE DISRUPTOR; MOXESTROL; PREGNENOLONE; SIMAZINE;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMICAL ANALYSIS; CHEMICAL STRUCTURE; CLASSIFIER; CONTROLLED STUDY; CORRELATION COEFFICIENT; FEASIBILITY STUDY; HORMONE ACTION; MACHINE LEARNING; PRIORITY JOURNAL; PROBABILITY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE;

EID: 67649219297     PISSN: 1611020X     EISSN: 16110218     Source Type: Journal    
DOI: 10.1002/qsar.200860090     Document Type: Article

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