Choosing feature selection and learning algorithms in QSAR

Journal of Chemical Information and Modeling

Volumn 54, Issue 3, 2014, Pages 837-843

  Eklund, Martin ^a,b   Norinder, Ulf ^c   Boyer, Scott ^b   Carlsson, Lars ^b  

^a UPPSALA UNIVERSITY   (Sweden)

^b ASTRAZENECA R AND D   (Sweden)

^c H LUNDBECK A S   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL CHEMISTRY; DECISION TREES; LARGE DATASET; LEARNING SYSTEMS; LINEAR REGRESSION; NEURAL NETWORKS; RANDOM FORESTS; SUPPORT VECTOR MACHINES;

EMPIRICAL INVESTIGATION; FEATURE SELECTION METHODS; GAIN PREDICTION; LEARNING METHODS; MULTIPLE LINEAR REGRESSIONS; PREDICTION ACCURACY; QSAR ANALYSIS; STATE OF THE ART;

FEATURE EXTRACTION;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; COMPUTER PROGRAM; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REGRESSION ANALYSIS; STATISTICAL MODEL; SUPPORT VECTOR MACHINE;

ALGORITHMS; LEAST-SQUARES ANALYSIS; LINEAR MODELS; NEURAL NETWORKS (COMPUTER); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOFTWARE; SUPPORT VECTOR MACHINES;

EID: 84896980988     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci400573c     Document Type: Article

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