AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia

Abstract

Neurodegenerative diseases like Alzheimer's, Parkinson's, and HIV-associated neurocognitive disorder severely impact patients and healthcare systems. While effective treatments remain limited, researchers are actively developing ways to slow progression and improve patient outcomes, requiring innovative approaches to handle huge volumes of new scientific data. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20,889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. We focused on repurposing drugs for dementia by identifying dementia-associated genes highly ranked in other disease classes. The method was developed for detection of genes that shared across multiple conditions and classified them based on their roles in biological pathways. This led to the selection of six primary drugs for further study.