Artificial neural network-based analysis of high-throughput screening data for improved prediction of active compounds

Journal of Biomolecular Screening

Volumn 14, Issue 10, 2009, Pages 1236-1244

  Chakrabarti, Swapan ^a   Svojanovsky, Stan R ^b   Slavik, Romana ^c   Georg, Gunda I ^d   Wilson, George S ^e   Smith, Peter G ^b,f  

^a University of Kansas   (United States)

^b UNIVERSITY OF KANSAS MEDICAL CENTER   (United States)

^c Advanced Response Management Inc   (United States)

^d UNIVERSITY OF MINNESOTA   (United States)

^e UNIVERSITY OF KANSAS   (United States)

^f UNIVERSITY OF KANSAS SCHOOL OF MEDICINE   (United States)

Author keywords

Generalization property; Neural networks; Pattern classification

Indexed keywords

METHIONYL AMINOPEPTIDASE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; ENZYME INHIBITION; HIGH THROUGHPUT SCREENING; PHYSICAL CHEMISTRY; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; VALIDATION STUDY;

DRUG DISCOVERY; HIGH-THROUGHPUT SCREENING ASSAYS; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 71949128923     PISSN: 10870571     EISSN: 1552454X     Source Type: Journal    
DOI: 10.1177/1087057109351312     Document Type: Article

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