Dopavision: A Novel Supervised Machine Learning Model for Predicting Post-Cocaine Phasic Mesolimbic Dopamine Activity in Male C57BL/6J Mice
ISEF · 2025 Computational Biology and Bioinformatics
Overview
Dysregulated dopamine in the mesolimbic system is linked to disorders like schizophrenia, depression, and addiction. Low phasic dopamine release correlates with reduced drug reward sensitivity and reduced motivation, but no reliable method exists to predict phasic dopamine levels. This study developed a prototype machine learning model, Dopavision, to predict percent changes in phasic dopamine release following common behavioral testing in mice. Using social and locomotor data, the multiple linear regression explained 60.18% of dopamine variability, while the random forest classifier algorithm achieved 83.33% accuracy. Future research can refine the model for both sexes, potentially enabling human DA prediction and early disorder prevention.
Competition history
- ISEF 2025
Resources
Related projects
ISEF · 2023
Cell Type-Specific Expression of the Molecular Players in Mouse Prefrontal Cortex During Cocaine Addiction
ISEF · 2025
A Novel Machine Learning Method and Drug for the Diagnosis and Treatment of Depression
ISEF · 2024
Combating the Drug Epidemic: A Machine-Learning Framework to Predict Novel Drug-Drug Interaction Risks of Illicit Drug Abuse
ISEF · 2019
A Novel Noninvasive and Inexpensive Biomarker for Diagnosing Major Depressive Disorder (MDD): Using Machine Learning Model in silico and Drosophila melanogaster Model in vivo
Closest projects by meaning, across every fair and year in the corpus.
Source: Regeneron International Science and Engineering Fair