Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods.
Zang, Qingda
Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods. [electronic resource] - Journal of chemical information and modeling Dec 2013 - 3244-61 p. digital
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
1549-960X
10.1021/ci400527b doi
Algorithms
Artificial Intelligence
Discriminant Analysis
Endocrine Disruptors--classification
Environmental Monitoring
High-Throughput Screening Assays
Humans
Quantitative Structure-Activity Relationship
Receptors, Estrogen--agonists
Risk Assessment
Water Pollutants, Chemical--classification
Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods. [electronic resource] - Journal of chemical information and modeling Dec 2013 - 3244-61 p. digital
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
1549-960X
10.1021/ci400527b doi
Algorithms
Artificial Intelligence
Discriminant Analysis
Endocrine Disruptors--classification
Environmental Monitoring
High-Throughput Screening Assays
Humans
Quantitative Structure-Activity Relationship
Receptors, Estrogen--agonists
Risk Assessment
Water Pollutants, Chemical--classification