Computational models to predict endocrine-disrupting chemical binding with androgen or oestrogen receptors

Ecotoxicology and Environmental Safety

Volumn 110, Issue , 2014, Pages 280-287

  Chen, Yingjie ^a   Cheng, Feixiong ^a   Sun, Lu ^a   Li, Weihua ^a   Liu, Guixia ^a   Tang, Yun ^a  

^a EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Androgen receptor; Endocrine disrupting chemicals; Machine learning; Oestrogen receptors; Substructure alert

Indexed keywords

ALKYL CHLORIDE; AMINE; ANDROGEN RECEPTOR; ANNELATED RING; ARYL CHLORIDE; ENDOCRINE DISRUPTOR; ESTROGEN RECEPTOR; KETONE; NITRO DERIVATIVE; PHENOL DERIVATIVE; TRIFLUOROMETHYL; UNCLASSIFIED DRUG; PROTEIN BINDING;

ACCURACY ASSESSMENT; ALGORITHM; ANDROGEN; CHEMICAL BINDING; ENDOCRINE DISRUPTOR; ENVIRONMENTAL RISK; ESTROGENIC COMPOUND; FREQUENCY ANALYSIS; NUMERICAL MODEL;

ACCURACY; AREA UNDER THE CURVE; ARTICLE; BAYESIAN LEARNING; BINARY CLASSIFICATION MODEL; BINDING AFFINITY; C4.5 DECISION TREE; CHEMICAL ANALYSIS; CHEMICAL PARAMETERS; COMPARATIVE STUDY; COMPUTER MODEL; CONTROLLED STUDY; INFORMATION GAIN; INFORMATION PROCESSING; K NEAREST NEIGHBOR; MACHINE LEARNING; MOLECULAR FINGERPRINT; PREDICTIVE VALUE; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RECEPTOR BINDING; STRUCTURE ANALYSIS; SUBSTRUCTURE FREQUENCY ANALYSIS; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; ALGORITHM; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CHEMICAL MODEL; CHEMISTRY; COMPUTER SIMULATION; HUMAN; METABOLISM; RISK FACTOR; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTER SIMULATION; ENDOCRINE DISRUPTORS; HUMANS; MODELS, CHEMICAL; PROTEIN BINDING; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RECEPTORS, ANDROGEN; RECEPTORS, ESTROGEN; RISK FACTORS; VALIDATION STUDIES AS TOPIC;

EID: 84907500336     PISSN: 01476513     EISSN: 10902414     Source Type: Journal    
DOI: 10.1016/j.ecoenv.2014.08.026     Document Type: Article

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