Breathing new life into asthma research: a review of machine learning and multi-omics approaches

INTRODUCTION: Asthma is a chronic respiratory disease that affects millions of people worldwide, and despite intensive study, the underlying molecular pathways are still unknown. Recent breakthroughs in machine learning and multi-omics technology provide new avenues for delving deeper into disease pathophysiology and identifying potential treatment targets. EVIDENCE ACQUISITION: We comprehensively reviewed the literature to explore the potential of machine learning and multi-omics approaches in asthma research. We searched the Scopus database using a combination of terms such as “asthma,” “machine learning”, and “multi-omics” and their synonyms. EVIDENCE SYNTHESIS: Our review revealed that machine learning and multi-omics approaches have been increasingly used to identify biomarkers, classify asthma subtypes, predict treatment outcomes, and understand the disease pathophysiology at the molecular level. CONCLUSIONS: Combining machine learning and multi-omics technologies holds tremendous potential for advancing our understanding of asthma pathogenesis and identifying novel therapeutic targets. Overall, this analysis demonstrates how these techniques have the potential to revitalize asthma research and improve patient outcomes. More study is needed, however, to confirm the utility of these approaches and understand how to build viable models that can be applied to clinical practice.