Biology subjects

Lodh, E.

Publications and source records attributed to Lodh, E..

3 recordsLinked to original sources

bioRxiv2026

Multiple Fault Analysis and Drug Therapy on Signaling Pathways Using Dynamic Bayesian Network-based Model

Cancer-associated signaling pathways often exhibit abnormal activation under simultaneous dysregulation of multiple molecular components. This study presents a probabilistic temporal Dynamic Bayesian Network (DBN)-based framework for analyzing multi-fault behaviour and intervention response in Growth Factor (GF) and Mitogen-Activated Protein Kinase (MAPK) signaling pathways. Unlike deterministic Boolean propagation, the proposed model represents each pathway component through an activation probability and propagates these probabilities over discrete time steps using soft-logic update rules. One-, two-, three-, and four-fault scenarios were systematically evaluated under a common lowest-burden input vector. The resulting output probabilities were summarized using an encoded pathway-burden score, and known-drug combinations were ranked using efficiency scores relative to no-intervention baselines. Pareto analysis was further used to balance intervention efficiency against drug-vector burden, while a custom dual-target search was performed to identify computational intervention hypotheses beyond predefined drug targets. Results showed that encoded burden increased with fault order in both pathways, with MAPK producing a higher baseline burden than GF. Among known-drug vectors, U0126+LY294002+Temsirolimus consistently emerged as the strongest low-burden candidate, achieving efficiency close to the maximum six-drug vector. Custom dual-target analysis identified ERK1/2+RPS6KB1 in GF and Raf+MEK1 in MAPK as high-impact computational target pairs. Runtime benchmarking showed that batched vectorized NumPy execution substantially improved scalability for higher-order fault simulations. Overall, the framework provides an interpretable and scalable approach for probabilistic pathway-level fault analysis and intervention prioritization.

bioinformatics↗

bioRxiv2026

NetPolicy-RL: Network-Informed Offline Reinforcement Learning for Pharmacogenomic Drug Prioritization

Large-scale pharmacogenomic screens provide extensive measurements of drug response across diverse cancer cell lines; however, most computational approaches emphasize point-wise sensitivity prediction or static ranking, which are poorly aligned with practical decision-making, where only a limited number of candidate drugs can be tested. We propose NetPolicy-RL, a biologically informed and decision-centric framework for pharmacogenomic drug prioritization that integrates network diffusion modeling with offline reinforcement learning. Drug selection for each cell line is formulated as an offline contextual bandit problem, enabling direct optimization of ranking quality rather than surrogate regression objectives. Mechanistic biological context is incorporated by propagating drug targets over curated interaction networks (STRING and Reactome) using random walk with restart, and combining the resulting diffusion profiles with cell-specific molecular importance derived from multi-omics data to compute network disruption scores. These biologically grounded signals are integrated with normalized drug response measurements to construct a joint state representation, which is optimized using an offline actor-critic architecture. Across held-out test splits, NetPolicy-RL consistently outperforms global ranking heuristics and learning-to-rank baselines, achieving statistically significant improvements in per-cell Normalized Discounted Cumulative Gain (NDCG@10) and substantial reductions in per-cell regret. Relative to GlobalTopK, the policy improves NDCG@10 for 88.7% of cell lines, while improvements exceed 95% compared with LambdaMART and regression-to-ranking baselines. Ablation analyses show that neither empirical response signals nor network-derived features alone are sufficient, and that their integration yields the most robust performance. Overall, this study demonstrates that combining mechanistic network biology with offline policy learning provides an effective and interpretable framework for drug prioritization in precision oncology.

bioinformatics↗

bioRxiv2026

miRNA-mRNA Interaction Network Analysis in Alzheimer's Disease for Biomarker Discovery

--Alzheimers disease (AD) is a complex neurodegenerative disorder characterized by widespread dysregulation of gene expression and regulatory pathways. MicroRNAs (miRNAs) act as key post-transcriptional regulators by modulating messenger RNAs (mRNAs), and their disruption can influence synaptic function, neuroinflammation, and neuronal survival. In this study, we present an integrative transcriptome-driven framework to identify AD-associated miRNA-mRNA regulatory signatures and potential biomarkers. Transcriptomic and clinical data were obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the GEO dataset GSE48552. Differential expression analysis (Welchs t-test with FDR correction) identified 148 significantly dysregulated genes (35 up-regulated, 113 down-regulated) between AD and cognitively normal controls. Experimentally validated and predicted miRNA-target interactions were integrated using miRTarBase and additional target resources, yielding 1,669,089 miRNA-gene interactions involving 3,055 unique miRNAs, with strong enrichment toward down-regulated gene targeting. Functional enrichment analysis revealed convergence of miRNA-regulated genes on synaptic signaling, neuronal communication, intracellular transport, apoptosis, oxidative stress, and PI3K-Akt/MAPK-related pathways. A bipartite miRNA-mRNA regulatory network (2,343 nodes; 14,603 edges) was constructed and analyzed using centrality metrics, highlighting key hub regulators including PBX1 and SLC7A5. Finally, supervised machine learning models trained on selected molecular features achieved strong performance, with ensemble approaches (XGBoost/LightGBM) demonstrating robust discrimination of AD from controls.

bioinformatics↗