Machine Learning–Driven Integration of Multi-Omics Data for Biomarker Identification in Rheumatoid Arthritis

Abstract

Purpose: In this study, the author explores the possibility of using multi-omics data combined with ML to identify biomarkers in RA, a chronic autoimmune disease characterized by chronic joint inflammation, destruction, and disability. The work specifically investigates novel biomarkers for putting into practice multi-omics (genomics, proteomics, and metabolomics) interfaced with machine learning algorithms to improve diagnostic accuracy, prognosis, and therapy management for RA patients. It becomes the direction of future RA omics and AI studies to enhance clinical efficacy and develop more effective therapeutic management.

Design/Methodology/Approach: The study elicits data from a cross-sectional survey of a sample of RA patients and HCPs using a closed set of standardized quantitative research questions. This research framework involves using existing RA datasets to combine multi-omics data, subsequently using Machine Learning algorithms to predict biomarkers and other molecular characteristics related to the disease. The questionnaire was also employed to obtain participants' perceptions on the possibility of applying multi-omics-based biomarker discovery in RA, its efficiency, and barriers to implementation. The survey was done among patients with RA, clinicians, and researchers to determine the clinician, patient, and researcher’s perception of using those technologies in clinical practice.

Implications: The current study demonstrated that using multi-omics data with machine learning can potentially improve RA research and management efforts. Concerning discovering new biomarkers that may help diagnose a condition earlier … technologies gave very high scores of interest. Machine learning models provided hypothesis generation and testing for many associations throughout the omics data and offered prognosis of disease course and treatment efficacy. However, data heterogeneity, technical issues, and the lack of large high-quality datasets were recognized as key barriers to the broader use.

Implications: The study has shown how multi-omics and machine learning integration need further advancement in RA biomarker identification. The study raises awareness of the need to enhance data availability, resolve the practical issues of integration between minus data, and implement explainable algorithms to aid decision-making. Experts in healthcare and research insist that strong guidelines should be established for omics data assessment to help integrate these tools into clinical practice in RA cases. In addition, assembling broader and more numerous datasets will be equally essential for increasing the efficiency of machine learning predictions.

Contribution/Novelty: The present paper fills the gap in applying precision medicine for rheumatoid arthritis by employing multi-omics integrated with machine learning. It sheds new light on how some of these third-generation technologies may be applied to biomarker discovery, prognosis, and RA’s individualized management. This study also stresses the need to effectively integrate, often, disciplinary, knowledge, and patient-oriented concerns in computational competence, clinical experience, and application of omics and AI in rheumatology.