Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods

Journal of Chemical Information and Modeling

Volumn 53, Issue 12, 2013, Pages 3244-3261

  Zang, Qingda ^a   Rotroff, Daniel M ^a,b   Judson, Richard S ^a  

^a U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

^b NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICALS; COMPUTATIONAL CHEMISTRY; DECISION TREES; DISCRIMINANT ANALYSIS; ENDOCRINE DISRUPTERS; INFORMATION MANAGEMENT; ORGANIC POLLUTANTS; RISK ASSESSMENT; STRUCTURES (BUILT OBJECTS); SUPPORT VECTOR MACHINES; SUPPORT VECTOR REGRESSION;

CLASSIFICATION AND REGRESSION TREE; ENVIRONMENTAL RISK ASSESSMENT; FEATURE SELECTION METHODS; HIGH-THROUGHPUT SCREENING; LINEAR DISCRIMINANT ANALYSIS; MACHINE LEARNING METHODS; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS;

LEARNING SYSTEMS;

ENDOCRINE DISRUPTOR; ESTROGEN RECEPTOR;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CLASSIFICATION; DISCRIMINANT ANALYSIS; DRUG ANTAGONISM; DRUG POTENTIATION; ENVIRONMENTAL MONITORING; HIGH THROUGHPUT SCREENING; HUMAN; METABOLISM; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RISK ASSESSMENT; WATER POLLUTANT;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DISCRIMINANT ANALYSIS; ENDOCRINE DISRUPTORS; ENVIRONMENTAL MONITORING; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RECEPTORS, ESTROGEN; RISK ASSESSMENT; WATER POLLUTANTS, CHEMICAL;

EID: 84896504827     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci400527b     Document Type: Article

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