Co-expression and Data Fusion Analysis of Omics Data for Liver Related Metabolic Diseases

In the proposed work co expression technique is applied to perform data fusion techniques to integrate omics data, understand gene and protein relationships, explore protein expression in human tissues, and identify drug targets. The need to comprehensively analyze diverse omics data—transcriptomics, proteomics, metabolomics using co-expression analysis and data fusion.Involves obtaining and processing data from the Protein Atlas website to extract information about protein subcellular location and gene expression in various cell lines. The fusion process combines these datasets based on a common "Gene” property, facilitating analysis to identify potential therapeutic targets, specifically for liver-related metabolic diseases. This approach utilizes feature-level fusion and leverages resources for research purposes, offering insights into protein functions, cellular processes, and potential drug targets. The integration of data sources and feature-level fusion, illuminating the intricate interplay between protein subcellular localization and gene expression across cell types and tissues. This fusion technique uncovers potential therapeutic targets for liver-related metabolic disorders. The Protein Atlas database, a valuable resource, aids researchers and clinicians. The correlation matrix offers insights into variable interactions and potential biomarkers. PCA provides dimensionality reduction and trend visualization, while WGCNA explores co-expression patterns in-depth. These techniques offer varying levels of complexity and insight, catering to different analytical goals.