Pathway-Based Gene Prioritization and Machine Learning Approaches for Rheumatoid Arthritis Analysis

Rheumatoid Arthritis (RA)is a chronic autoimmune disorder. Genetic factors and signalling pathways influence its progression. This study integrates pathway-based gene prioritization and machine learning models. KEGG pathway enrichment identifies key RA-related pathways. T-cell receptor signalling and cytokine interactions play crucial roles. Network constructs a gene-pathway interaction network. Centrality measures highlight hub genes. Degree and betweenness centralities determine gene significance. Machine learning models rank genes based on importance. Long Short-Term Memory(LSTM) Rank Net achieves the highest correlation (0.9713). VEGFA, MMP3, and IFNG, HLA emerge as top-ranked genes. Pathway enrichment analysis confirms their role in immune modulation. Graph-based methods enhance systematic gene ranking. The study underscores the power of integrating bioinformatics and AI. Future work will explore graph neural networks and multi-omics data. These approaches refine gene prioritization for RA treatment. Machine learning models optimize predictions for disease mechanisms. Network-based analysis improves understanding of gene interactions. Identified genes may serve as potential therapeutic targets. Further research can validate their clinical applications. Drug repurposing strategies could leverage these findings. Multi-omics datasets enhance the robustness of predictive models. Combining genetic insights with AI-driven methods advances RA research. Integrating pathway data strengthens the biological relevance of findings. This framework offers a scalable approach for precision medicine. Developing targeted therapies may benefit from these prioritized genes. Future advancements in AI will refine gene ranking accuracy. Understanding RA pathogenesis requires integrating multiple data sources. This research provides a foundation for enhanced RA diagnostics and treatment.