# Classifying addictions using machine learning analysis of multidimensional data

> **NIH NIH K02** · UNIVERSITY OF CONNECTICUT STORRS · 2021 · $158,923

## Abstract

ABSTRACT
This Independent Scientist Award will significantly enhance my research capabilities, enabling me to become a
leading quantitative investigator in the field of substance use disorders (SUDs). Specifically, it will allow me to
increase my knowledge in the areas of SUD phenotypes, treatment and genetics. SUDs are clinically and
etiologically heterogeneous and their classification has been difficult. This application reflects my ongoing
commitment to developing an innovative and interdisciplinary research program on the classification of SUDs
through quantitative analysis of multidimensional data. My extensive training in computational science and
prior research on biomedical informatics have provided me with the skills to design, implement and evaluate
advanced algorithms and sophisticated analyses to solve challenging problems in classifying SUDs. My
ongoing NIDA-funded R01 employs a large (n=~12,000) sample aggregated from multiple genetic studies of
cocaine, opioid, and alcohol dependence to develop and evaluate novel statistical models to generate clinical
SUD subtypes that are optimized for gene finding. This K02 proposal extends that work to evaluate treatment
outcome in refined subgroups of SUD populations using data from treatment studies for cocaine, opioid,
alcohol and multiple substance dependence. This project will integrate data from diagnostic behavioral
variables and genotypes, as well as biological/neurobiological features of the disorders and repeated
measures of treatment outcome. The primary career development goals of this application are to: (1)
understand the reliability, validity and functional mechanisms of various phenotyping methods; (2) to continue
training in the genetics of addictions; and (3) to gain greater knowledge of different treatment approaches and
their efficacy. A solid foundation in these areas will enhance my ability to realize the full potential of the data
collected and aggregated from multiple dimensions, and to use the data to design the most clinically useful
analysis and generate innovative solutions to diagnostic and predictive challenges in SUD research. Through
formal coursework, directed readings, individual tutoring and intensive multidisciplinary collaboration with a
diverse team of world-renowned researchers, I will receive training and collect pilot data for future R01 projects
by examining (Aim I): whether clinically-defined highly heritable subtypes derived in my current R01 project
predict differential treatment response; (Aim II) whether new statistical models that directly combine treatment
data with behavioral, biological, and genomic data identify refined subtypes with confirmatory multilevel
evidence; and (Aim III) whether there are genetic and social moderators of treatment outcome by subtype. The
overall goal of this proposal is to further my independent and multidisciplinary research program in the
development of statistical methods for refined classification of SUDs. The K0...

## Key facts

- **NIH application ID:** 10087504
- **Project number:** 5K02DA043063-05
- **Recipient organization:** UNIVERSITY OF CONNECTICUT STORRS
- **Principal Investigator:** Jinbo Bi
- **Activity code:** K02 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $158,923
- **Award type:** 5
- **Project period:** 2017-02-15 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10087504

## Citation

> US National Institutes of Health, RePORTER application 10087504, Classifying addictions using machine learning analysis of multidimensional data (5K02DA043063-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10087504. Licensed CC0.

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