WORLD JOURNAL OF ADVANCE
HEALTHCARE RESEARCH

Dr. Sharique Ahmad*, Raushan Kumar Tanish Baqar, Pushpendra D. Pratap

ABSTRACT

There has been a consistent increase in the prevalence of chronic diseases, encompassing both noninfectious and infectious conditions, which has necessitated the adoption of a multidisciplinary approach to comprehending and managing these ailments. The present state of medical care emphasizes the treatment of individuals once they have transitioned into patients, rather than prioritizing preventive measures to mitigate the occurrence of illnesses. This approach has resulted in substantial financial burdens associated with the management of chronic ailments and diseases that have reached advanced stages. Moreover, the implementation of a universal health care system fails to include the unique variations in genetics, environment, and lifestyle characteristics across individuals, hence limiting the potential number of individuals who can derive benefits from such interventions. The rapid advancements in omics technology and the improvement of computational skills have facilitated the emergence of multi-omics deep phenotyping. This method enables the comprehensive analysis of the interplay between several biological levels throughout time, hence enhancing the effectiveness of precision health strategies. This review provides an overview of the current technical classifications of multi-omics, highlights experimental design considerations, emphasizes the integrated analysis techniques used in multi-omics research, particularly the use of machine learning and deep learning to integrate multi-omics data, and discusses the tools involved. It also explores the applications of multi-omics in medical research, particularly in the areas of cancer, neurodegenerative diseases, and aging.

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