Introduction to artificial intelligence in multi-omics analysis.

Abstract

Multi-omics refers to various high-throughput datasets, including genomics, transcriptomics (Bulk, single-cell, spatial), proteomics, and metabolomics, which are used to understand complex biological systems at multiple molecular levels. This chapter focuses on different open-source tools, corresponding databases, and standardized bioinformatics pipelines for each omics data analysis. It describes how different machine learning algorithms, such as supervised, unsupervised, and reinforcement learning approaches, are employed to extract meaningful features for predicting disease phenotype and potential biomarkers. Furthermore, this chapter discusses the challenges with omics data analysis using machine learning algorithms and examines different strategies for integrating the multi-omics dataset with machine learning methods. It also described various AI-based tools and frameworks that can be employed to analyze multi-omics datasets. The chapter concludes with current studies and future directions of analyzing omics datasets using artificial intelligence techniques.