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Molecular Informatics

Volumn 32, Issue 9-10, 2013, Pages 855-865

Physicochemical vs. vibrational descriptors for prediction of odor receptor responses

(5) Gabler, Stephan a,b Soelter, Jan a Hussain, Taufia c Sachse, Silke c Schmuker, Michael a,b

a FREIE UNIVERSITÄT BERLIN (Germany)

b BERNSTEIN CENTER FOR COMPUTATIONAL NEUROSCIENCE (Germany)

c MAX PLANCK INSTITUTE FOR CHEMICAL ECOLOGY (Germany)

Author keywords

EVA; Machine learning; Odor space; Structure activity relationships; Virtual screening

Indexed keywords

ARTIFICIAL INTELLIGENCE; E-LEARNING; ENCODING (SYMBOLS); FORECASTING; ODORS; SIGNAL ENCODING;

DESCRIPTORS; ENCODINGS; EVA; FEATURES SETS; MACHINE-LEARNING; ODOR SPACE; OLFACTORY RECEPTORS; STRUCTURE-ACTIVITY RELATIONSHIPS; SUPPORT VECTOR REGRESSIONS; VIRTUAL SCREENING;

LEARNING SYSTEMS;

ANIMAL EXPERIMENT; ARTICLE; BIOMETRY; COMPUTER PROGRAM; CONTROLLED STUDY; DROSOPHILA; IN VIVO STUDY; INFORMATION PROCESSING; KERNEL METHOD; NONHUMAN; ODOR; OLFACTORY RECEPTOR; PARAMETERS; PERFORMANCE; PHYSICAL CHEMISTRY; POST HOC ANALYSIS; PREDICTION; PRIORITY JOURNAL; RANDOM FOREST; RANDOM FOREST REGRESSION; SMELLING; SUPPORT VECTOR MACHINE; VIBRATION;

EID: 84892448521 PISSN: 18681743 EISSN: 18681751 Source Type: Journal
DOI: 10.1002/minf.201300037 Document Type: Article

Times cited : (14)

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