Machine learning for precision medicine forecasts and challenges when incorporating non omics and omics data

Abstract

Precision Medicine has emerged as a preventive, diagnostic and treatment tool to approach human diseases in a personalized manner. Since precision medicine incorporates omics data and knowledge in personal health records, people who live in industrially polluted areas have an advantage in the medicinal field. Integration of non-omics data and related biological knowledge in term omics data is a reality. The heterogenic characteristics of non-omics data and high dimensional omics data makes the integration challengeable. Hard data analytics problems create better opportunities in analytics. This review cut across the boundaries of machine learning models for the eventual development of a successful precision medicine forecast model, different strategies for the integration of non-omics data and omics data, limitations and challenges in data integration, and future directions for the precision medicine forecasts. The literature also discusses non-omics data, diseases associated with air pollutants, and omics data. This information gives insight to the integrated data analytics and their application in future project implications. It intends to motivate researchers and precision medicine forecast model developers in a global integrative analytical approach.