Shreeya Pahune supervised by Dr. Bhaswar Ghosh received her Master of Science – Dual Degree  in Computational Natural Sciences (CND). Here’s a summary of her research work on From Genes to Drugs: A Machine Learning and Network Science Approach to Understanding and Treating Malaria:

 Malaria, caused by Plasmodium falciparum, remains a major global health challenge, requiring both a deeper understanding of parasite biology and innovative therapeutic strategies. This thesis presents a dual-pronged computational approach to address this challenge by combining transcriptomic analysis of Plasmodium falciparum with network-driven drug repurposing methodologies to address these gaps. First, we analyze single-cell RNA sequencing (scRNA-seq) data from the Malarial Cell Atlas to gain comprehensive insights into P. falciparum’s complex life cycle. Using feature selection techniques and a neural network classifier, we extract high-confidence gene sets that capture stage-specific signatures with high accuracy and outperform the gene set, confirming the capture of distinctive stage-specific signatures. Functional enrichment and pathway analyses further validate their biological relevance, linking distinct gene sets to immune evasion, haemoglobin digestion, merozoite invasion, and sexual differentiation. Next, we implemented a network-driven drug repurposing framework to identify repurposable drugs for malaria. We constructed an integrated graph combining Protein-Protein Interaction networks, Drug-Target Protein graphs, Disease-Protein associations, and Drug-Disease links to predict potential antimalarial drug candidates. Comparative analysis of several Graph Neural Network architectures, including Graph Convolutional Networks, Graph Attention Networks, GraphSAGE, and Graph Isomorphism Networks, showed comparable performance with AUC-ROC values around 0.98. However, when the task was framed as a recommendation problem using Matrix Factorization with side information, performance decreased significantly, highlighting the critical importance of incorporating protein-protein interactions in the modelling process. These findings underscore the power of combining transcriptomic insights with computational drug discovery. By refining gene selection for parasite stage characterization and leveraging network-based drug repurposing, this work provides a data-driven framework for identifying potential malaria therapeutics. Future research should focus on experimental validation and extending these methodologies to other infectious diseases.

June 2025