A ternary classification using machine learning methods of distinct estrogen receptor activities within a large collection of environmental chemicals

Science of the Total Environment

Volumn 580, Issue , 2017, Pages 1268-1275

  Zhang, Quan ^a   Yan, Lu ^a   Wu, Yan ^c   Ji, Li ^b   Chen, Yuanchen ^a   Zhao, Meirong ^a   Dong, Xiaowu ^c  

^a ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c ZHEJIANG UNIVERSITY   (China)

Author keywords

Estrogen receptor activities; Machine learning methods; Ternary classification

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHEMICALS; DISCRIMINANT ANALYSIS; ENVIRONMENTAL PROTECTION AGENCY; LEARNING SYSTEMS; SUPPORT VECTOR MACHINES;

10-FOLD CROSS-VALIDATION; CLASSIFICATION AND REGRESSION TREE; ENDOCRINE DISRUPTING CHEMICALS; ENVIRONMENTAL CHEMICALS; ESTROGEN RECEPTOR; LINEAR DISCRIMINANT ANALYSIS; MACHINE LEARNING METHODS; MACHINE LEARNING MODELS;

ENDOCRINE DISRUPTERS;

ENDOCRINE DISRUPTOR; ESTROGEN RECEPTOR;

ENDOCRINE DISRUPTOR; ESTROGENIC COMPOUND; MACHINE LEARNING; NUMERICAL MODEL; SUPPORT VECTOR MACHINE;

AGONIST POTENCY; ANTAGONIST POTENCY; ARTICLE; CHEMICAL ANALYSIS; CLASSIFICATION; CLASSIFICATION AND REGRESSION TREE; CONTROLLED STUDY; ESTROGEN ACTIVITY; HUMAN; HUMAN CELL; LINEAR DISCRIMINANT ANALYSIS; MACHINE LEARNING; MEASUREMENT ACCURACY; PREDICTION; SCREENING TEST; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; CHEMISTRY; DISCRIMINANT ANALYSIS;

DISCRIMINANT ANALYSIS; ENDOCRINE DISRUPTORS; HUMANS; MACHINE LEARNING; RECEPTORS, ESTROGEN; SUPPORT VECTOR MACHINE;

EID: 85008219804     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2016.12.088     Document Type: Article

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