GWAS-based machine learning approach to predict duloxetine response in major depressive disorder

Journal of Psychiatric Research

Volumn 99, Issue , 2018, Pages 62-68

  Maciukiewicz, Malgorzata ^a   Marshe, Victoria S ^a,b   Hauschild, Anne Christin ^c,d,e   Foster, Jane A ^e,f   Rotzinger, Susan ^d,e   Kennedy, James L ^a,b,d   Kennedy, Sidney H ^b,d,e,g   Müller, Daniel J ^a,b,d   Geraci, Joseph ^h  

^a CENTRE FOR ADDICTION AND MENTAL HEALTH   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c IBM CANADA LTD   (Canada)

^d UNIVERSITY OF TORONTO   (Canada)

^e UNIVERSITY HEALTH NETWORK   (Canada)

^f MCMASTER UNIVERSITY   (Canada)

^g ST MICHAEL'S HOSPITAL   (Canada)

^h QUEEN S UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DULOXETINE; PLACEBO; SEROTONIN NORADRENALIN REUPTAKE INHIBITOR;

ADULT; ARTICLE; FEMALE; GENOME-WIDE ASSOCIATION STUDY; GENOTYPING TECHNIQUE; HUMAN; MACHINE LEARNING; MAJOR CLINICAL STUDY; MAJOR DEPRESSION; MALE; MONTGOMERY ASBERG DEPRESSION RATING SCALE; PHARMACOGENETICS; PRIORITY JOURNAL; PSYCHOPHARMACOTHERAPY; REMISSION; SUPPORT VECTOR MACHINE; TREATMENT DURATION; TREATMENT OUTCOME; TREATMENT RESPONSE; GENETICS; MIDDLE AGED; OUTCOME ASSESSMENT; PROCEDURES; PROGNOSIS; SENSITIVITY AND SPECIFICITY; SINGLE NUCLEOTIDE POLYMORPHISM; STANDARDS;

ADULT; DEPRESSIVE DISORDER, MAJOR; DULOXETINE HYDROCHLORIDE; FEMALE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MALE; MIDDLE AGED; OUTCOME ASSESSMENT (HEALTH CARE); POLYMORPHISM, SINGLE NUCLEOTIDE; PROGNOSIS; SENSITIVITY AND SPECIFICITY; SEROTONIN AND NORADRENALINE REUPTAKE INHIBITORS; SUPPORT VECTOR MACHINE;

EID: 85041460311     PISSN: 00223956     EISSN: 18791379     Source Type: Journal    
DOI: 10.1016/j.jpsychires.2017.12.009     Document Type: Article

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