Machine learning tools, models, and algorithms for early detection of alzheimer’s disease in correlation with multi omics data

Alzheimer's disease (AD) is a neurodegenerative condition characterized by a steady deterioration in cognitive function that eventually results in loss of autonomy and death. AD is the leading cause of dementia, affecting more than 50 million individuals globally. Multi-omics data have been shown to be important in comprehending the complexity of AD in earlier research. With distinct insights from each omic layer, multi-omics data—which include transcriptomics, proteomics, metabolomics, and genomics—offer a complete picture of the molecular mechanisms driving AD. In order to understand the intricate molecular mechanisms behind AD, 700 tissue samples were subjected to Multi-Omics Factor Analysis (MOFA) in this investigation. MOFA discovered 10 latent variables that capture unique differences both within and across these omic layers by combining mRNA, miRNA, and lncRNA data. This integrated method emphasizes how important multi-omics are to understanding disease causes and finding putative biomarkers. The results of the analysis demonstrated that tissue-specific variations account for a significant portion of the variance explained by these latent variables, underscoring the need of taking these variations into account when researching complex disorders such as AD. These results highlight MOFA's potential as an effective method for locating treatment targets and biomarkers, which will further AD research and enhance patient outcomes.