In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods

Chemosphere

Volumn 82, Issue 11, 2011, Pages 1636-1643

  Cheng, Feixiong ^a   Shen, Jie ^a   Yu, Yue ^a   Li, Weihua ^a   Liu, Guixia ^a   Lee, Philip W ^a,b   Tang, Yun ^a  

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

^b KYOTO UNIVERSITY   (Japan)

Author keywords

Information gain; Machine learning; Quantitative structure toxicity relationship; Substructure pattern recognition; Support vector machine; Tetrahymena pyriformis toxicity

Indexed keywords

ACCIDENT PREVENTION; DECISION TREES; INDUSTRIAL CHEMICALS; LEARNING SYSTEMS; MACHINE LEARNING; NEAREST NEIGHBOR SEARCH; ORGANIC CHEMICALS; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES; TOXICITY;

C4.5 DECISION TREES; ENVIRONMENTAL HAZARDS; INFORMATION GAIN; MACHINE LEARNING METHODS; QUANTITATIVE STRUCTURES; RADIAL BASIS FUNCTIONS; SUBSTRUCTURE PATTERNS; TETRAHYMENA PYRIFORMIS;

PATTERN RECOGNITION;

INDUSTRIAL CHEMICAL;

ALGORITHM; ASSESSMENT METHOD; BIOACCUMULATION; CHEMICAL POLLUTANT; CILIATE; DATA SET; ENVIRONMENTAL HAZARD; FOREST ECOSYSTEM; NUMERICAL MODEL; QUANTITATIVE ANALYSIS; TOXICITY;

ALGORITHM; ARTICLE; CLASSIFICATION; COMPUTER MODEL; CONTROLLED STUDY; DECISION TREE; K NEAREST NEIGHBOR; NONHUMAN; POLLUTION; RANDOM FOREST; RANDOMIZED CONTROLLED TRIAL; SUPPORT VECTOR MACHINE; TETRAHYMENA PYRIFORMIS; TOXICITY;

TETRAHYMENA PYRIFORMIS;

EID: 79951721392     PISSN: 00456535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemosphere.2010.11.043     Document Type: Article

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