Adebayo_Research_MultiOmicsAnalysis_2024.pdf

this project base on order to find trends and insights that can enhance predictive modeling in a variety of biological and medical contexts, this research focuses on using genomic and epigenomic data for predictive analysis. Predictions about disease outcomes, patient responses to treatments, or biological traits are made more accurate and reliable by combining both genomic (DNA sequence and mutations) and epigenomic (changes affecting gene expression without changing the DNA sequence) omics data.In order to find trends and insights that can enhance predictive modeling in a variety of biological and medical contexts, this research focuses on using genomic and epigenomic data for predictive analysis. Predictions about disease outcomes, patient responses to treatments, or biological traits are made more accurate and reliable by combining both genomic (DNA sequence and mutations) and epigenomic (changes affecting gene expression without changing the DNA sequence) omics data. To find important traits that influence predicted accuracy, key techniques include statistical modeling, machine learning algorithms, and multi-omics data integration. This research investigates how integrating genomic and epigenomic data can enhance predictions, such as disease susceptibility or individualized treatment plans, and result in a more thorough understanding of biological processes.To highlight model performance, feature relevance, and the accuracy gain from multi-omics data integration, the results will be presented utilizing tables, graphs, and charts.