Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence.
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Utility of Machine-Learning Approaches to Identify Behavioral Markers for Substance Use Disorders: Impulsivity Dimensions as Predictors of Current Cocaine DependenceAddiction, cognitive decline and therapy: seeking ways to escape a vicious cycle.Alterations of resting state functional network connectivity in the brain of nicotine and alcohol users.Development of New Diagnostic Techniques - Machine Learning.The Opioid Abuse Risk Screener predicts aberrant same-day urine drug tests and 1-year controlled substance database checks: A brief report.Hierarchical investigation of genetic influences on response inhibition in healthy young adults.Locomotor activity does not predict individual differences in morphine self-administration in rats.e-Addictology: An Overview of New Technologies for Assessing and Intervening in Addictive Behaviors.
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Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence.
description
article científic
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article scientifique
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articolo scientifico
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artigo científico
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bilimsel makale
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scientific article published on 15 February 2016
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vedecký článok
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vetenskaplig artikel
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videnskabelig artikel
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vědecký článek
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name
Machine-learning identifies su ...... iate and stimulant dependence.
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Machine-learning identifies su ...... iate and stimulant dependence.
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type
label
Machine-learning identifies su ...... iate and stimulant dependence.
@en
Machine-learning identifies su ...... iate and stimulant dependence.
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prefLabel
Machine-learning identifies su ...... iate and stimulant dependence.
@en
Machine-learning identifies su ...... iate and stimulant dependence.
@nl
P2860
P1476
Machine-learning identifies su ...... iate and stimulant dependence.
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Woo-Young Ahn
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P304
P356
10.1016/J.DRUGALCDEP.2016.02.008
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2016-02-15T00:00:00Z