A Novel Machine Learning Approach of Multi-omics Data Prediction

Abstract

An integrated (combined) approach is needed in today's biomedical research in order to effectively use this data and obtain insights into natural systems. Genome, proteome, and metaproteomic data may be used to comprehend the complexities of molecular genetics utilising "machine learning" performance tracking methods derived from diverse omics sources. New biomarkers may be discovered by merging and analysing omics data using machine learning techniques. These biomarkers may aid in the proper identification of illness, the separation of patients, and the provision of tailored therapy. This study looks at a variety of "integrative machine learning or ML techniques" that are being used to obtain a better understanding of biological systems during natural bodily performance as well as when systems are diseased.Secondary data collection method has been used for this paper to gather relevant and factual data related to ML techniques of multi omics data prediction.