Using Big Data to Identify Genetic, Neural Bases for Substance Use Disorder


Article Excerpt: One of the challenges for researchers studying SUDs (substance use disorders) is that there might be different underlying mechanisms or pathways that cause someone to become addicted to a certain substance, and the specific neurological and genetic factors that account for heterogeneous clinical manifestation are poorly understood. Professor Jinbo Bi in the Department of Computer Science and Engineering at the University of Connecticut has received a $1.7 million grant from the National Institute on Drug Abuse to develop machine learning algorithms to help identify SUD subcategories based on clinical, neuroimaging, and genetic data.

Full Article:

Article Source: Mirage News