Abstract
Our understanding of intricate biological systems has been completely transformed by the development of multi-omics approaches, which entail the simultaneous study of several different molecular data types. However, there are many obstacles to overcome when analyzing multi-omics data, including the requirement for sophisticated data processing and analysis tools. The integration of multi-omics research with artificial intelligence (AI) has the potential to fundamentally alter our understanding of biological systems. AI has emerged as an effective tool for evaluating complicated data sets. The application of AI and data processing techniques in multi-omics analysis is explored in this study. The present study articulates the diverse categories of information generated by multi-omics methodologies and the intricacies involved in managing and merging these datasets. Additionally, it looks at the various AI techniques-such as machine learning, deep learning, and neural networks-that have been created for multi-omics analysis. The assessment comes to the conclusion that multi-omics analysis has a lot of potential to change with the integration of AI and data processing techniques. AI can speed up the discovery of new biomarkers and therapeutic targets as well as the advancement of personalized medicine strategies by enabling the integration and analysis of massive and complicated data sets. The necessity for high-quality data sets and the creation of useful algorithms and models are some of the difficulties that come with using AI in multi-omics study. In order to fully exploit the promise of AI in multi-omics analysis, more study in this area is required.