Comparative Analysis of Machine Learning Approaches for Molecular Pathway Identification and Biomarker Discovery in Immune-Related Diseases

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Published: 2026-02-13
Keywords:
machine learning, molecular pathway analysis, biomarker discovery, immune-related diseases

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Haofeng Ye
Bioinformatics, Johns Hopkins University, MD, USA

Abstract

Immune-related diseases pose significant diagnostic challenges due to complex molecular mechanisms and heterogeneous clinical presentations. Machine learning approaches have emerged as powerful tools for molecular pathway identification and biomarker discovery. This comparative study evaluates five machine learning algorithms using transcriptomic datasets from rheumatoid arthritis, systemic lupus erythematosus, and inflammatory bowel disease. We assess algorithm performance across accuracy, computational efficiency, biological relevance, and clinical validity. Graph neural networks achieved superior disease classification performance (AUC: 0.847) and identified 21 significantly enriched pathways, compared to traditional clustering methods (classification AUC: 0.762, 17 pathways identified). Results establish practical guidelines for algorithm selection, advancing personalized diagnostic development.

Article Details

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Vol. 2 No. 1 (2026)

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Articles

How to Cite

Comparative Analysis of Machine Learning Approaches for Molecular Pathway Identification and Biomarker Discovery in Immune-Related Diseases. (2026). Journal of Science, Innovation & Social Impact, 2(1), 46-63. https://sagespress.com/index.php/JSISI/article/view/81

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