QUANTITATIVE GENOMIC MODELS FOR PREDICTION OF DISEASE SUSCEPTIBILITY USING MULTI-OMICS DATA

Abstract

Background: Complex diseases such as cardiovascular diseases, diabetes and cancer are regulated by multiple genetic, molecular and environmental factors. Conventional single-omics genomic studies frequently do not account for the intricate biological interactions related to disease susceptibility. Recent advances in the multi-omics technologies and computational biology have made it possible to integrate genomic, transcriptomic, proteomic, metabolomic and epigenomic data in a holistic way for improved predictive healthcare.
Objective: The goal of this study is to develop quantitative genomic models combining multi-omics datasets and machine learning algorithms for accurate prediction of disease susceptibility.

Methodology:  Multi-omics datasets were collected from public genomic repositories and clinical cohorts. Data preprocessing, feature selection, network-based analysis and deep learning frameworks were used to identify predictive biomarkers and disease-associated molecular signatures.

Findings: Compared with conventional single-omics approaches, the integrated multi-omics models significantly improved the predictive accuracy of cardiovascular disease (92%), type 2 diabetes (89%), cancer susceptibility (94%) and neurodegenerative disorders (90%). Discovery of biomarkers and interpretation of pathways was also significantly improved.

Conclusion: Integrated multi-omics data provides a robust framework for disease susceptibility prediction, personalized medicine and precision healthcare applications through quantitative genomic modeling.