A data mining approach to in vivo classification of psychopharmacological drugs

Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology
Neri KafkafiGreg I Elmer

Abstract

Data mining is a powerful bioinformatics strategy that has been successfully applied in vitro to screen for gene-expression profiles predicting toxicological or carcinogenic response ('class predictors'). In this report we used a data mining algorithm named Pattern Array (PA) in vivo to analyze mouse open-field behavior and characterize the psychopharmacological effects of three drug classes--psychomotor stimulant, opioid, and psychotomimetic. PA represents rodent movement with approximately 100,000 complex patterns, defined as multiple combinations of several ethologically relevant variables, and mines them for those that maximize any effect of interest, such as the difference between drug classes. We show that PA can discover behavioral predictors of all three drug classes, thus developing a reliable drug-classification scheme in small group sizes. The discovered predictors showed orderly dose dependency despite being explicitly mined only for class differences, with the high doses scoring 4-10 standard deviations from the vehicle group. Furthermore, these predictors correctly classified in a dose-dependent manner four 'unknown' drugs (ie that were not used in the training process), and scored a mixture of a psychomotor stimu...Continue Reading

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Citations

Aug 21, 2013·Psychopharmacology·Neri KafkafiGreg I Elmer
Mar 20, 2009·Schizophrenia Bulletin·Greg I Elmer, Neri Kafkafi
Aug 21, 2013·Biochemical Pharmacology·Paul McGonigle, Bruce Ruggeri
Apr 1, 2015·European Journal of Pharmacology·Suzanne M PetersBerry M Spruijt
May 3, 2015·Pharmacological Reports : PR·Javad MahmoudiSaeed Sadigh-Eteghad
Jan 5, 2014·Journal of Neuroscience Methods·Jesse BrodkinChristian Gutzen
Aug 8, 2018·Genes, Brain, and Behavior·Magdalena ZygmuntMichał Korostyński
Feb 3, 2009·The Journal of Pharmacology and Experimental Therapeutics·S J Enna, M Williams

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