miRDrug: Comprehensive Analysis of the Shared Genes within miRNA-Drug Pairs Using Grouping, Scoring, and Modeling Approach

Abstract

\ miRDrug is a novel computational tool aimed at elucidating the complex interactions between microRNAs (miRNAs), diseases, and drugs. Leveraging an innovative Grouping-Scoring-Modeling (G-S-M) approach and integrating data from three biological databases, miRDrug highlights the genes at the intersection of miRNAs and drug resistance pathways. This methodology systematically performs the analysis in three stages: initially, it groups genes based on shared biological characteristics and their relevance to miRNAs and drug resistance pathways, thereby harnessing the collective power of biological knowledge. Subsequently, it evaluates these groups through a scoring mechanism that quantitatively assesses their potential impact on distinguishing disease-specific phenotypes, with a particular focus on their role in drug resistance. Finally, the modeling phase employs machine learning techniques to construct predictive models based on the highest-scoring gene groups, where these genes serve as novel therapeutic targets. This endeavor deepens our understanding of miRNA roles in disease mechanisms. It holds promise for advancing personalized medicine by pinpointing novel therapeutic targets, especially in cancer treatment, where drug resistance remains a formidable challenge. Through computational analysis and literature-supported interpretation, miRDrug demonstrates its potential to support the prioritization of biologically meaningful miR-NA-drug-gene relationships that may guide future studies on targeted treatment strategies. This study underscores the importance of integrating diverse biological datasets to improve the interpretation of disease-related molecular mechanisms and to support the generation of hypotheses for future therapeutic investigations. Our research has culminated in developing a tool hosted on the KNIME platform, showcasing the practical application of our findings using miRDrug. With its user-friendly and modular design, miRDrug invites the broader scientific community to engage with, explore, and further expand our work. miRDrug is available for public access on GitHub at: https://github.com/malikyousef/miRDrug, fostering a collaborative space for research enhancement and innovation and inviting enthusiasts and researchers alike to contribute to the ongoing dialogue in the field.