PA-18-057

https://grants.nih.gov/grants/guide/pa-files/PA-18-057.html

NIDA

Nov 01 2017

May 08 2019

R01

The purpose of this FOA is to encourage the application of Big Data analytics to reveal deeper or novel insights into the biological and behavioral processes associated with substance abuse and addiction.NIDA recognizes that to accelerate progress toward understanding how the human brain and behavior is altered by chronic drug use and addiction, it is vital to develop more powerful analytical methods and visualization tools that can help capture the richness of data being generated from genetic, epigenetic, molecular, proteomic, metabolomic, brain-imaging, micro-electrode, behavioral, clinical, social, services, environmental studies as well as data generated from electronic health records.Applications for this FOA should develop and/or utilize computational approaches for analyzing large, complex datasets acquired from drug addiction research.The rapid increase of technologies to acquire unprecedented amounts of neurobiological and behavioral data, and an expanding capacity to store those data, results in great opportunity to bring to bear the power of the computational methods of Big Data analytics on drug abuse and addiction.

data sciencebig datacomputationalbioinformaticsmachine learning