Using machine learning tools to model complex toxic interactions with limited sampling regimes

Environmental Science and Technology

Volumn 47, Issue 6, 2013, Pages 2728-2736

  Bertin, Matthew J ^a   Moeller, Peter ^a   Guillette, Louis J ^a,b   Chapman, Robert W ^a  

^a NOAA Hollings Marine Laboratory   (United States)

^b MEDICAL UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; LEARNING SYSTEMS; METABOLITES; NEURAL NETWORKS;

BIOLOGICAL RESPONSE; CONDITION; ENVIRONMENTAL STRESS; HYPERSPACES; LABORATORY EXPERIMENTS; LEARNING TOOL; MACHINE-LEARNING; MODEL COMPLEXES; NATURAL SYSTEMS; TOXIC INTERACTIONS;

DESIGN OF EXPERIMENTS;

TOXIN;

ENVIRONMENTAL STRESS; EXPERIMENTAL STUDY; LABORATORY METHOD; NUMERICAL MODEL; PARAMETERIZATION; SAMPLING; STATISTICAL ANALYSIS; TOXIC MATERIAL;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGY; EXPERIMENT; LABORATORY; MACHINE LEARNING; MATHEMATICAL MODEL;

EID: 84875341996     PISSN: 0013936X     EISSN: 15205851     Source Type: Journal    
DOI: 10.1021/es3033549     Document Type: Article

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