Integrative Analysis of Multi-Omics Data with Deep Learning: Challenges and Opportunities in Bioinformatics.

Abstract

The advent of high-throughput technologies has ushered in an era of unprecedented data generation in the field of bioinformatics. Omics data, including genomics, transcriptomics, proteomics, and metabolomics, provide comprehensive insights into biological systems, but their integration poses significant challenges. Integrative analysis of multi-omics data holds the promise of unraveling complex biological phenomena and enabling personalized medicine. [1] Deep learning, a subset of machine learning, has gained prominence in bioinformatics due to its ability to automatically extract intricate patterns from large-scale multi-omics datasets. This paper presents an overview of the challenges and opportunities associated with the integrative analysis of multi-omics data using deep learning techniques in bioinformatics.The challenges in multi-omics integration primarily stem from data heterogeneity, dimensionality, and noise. One of the key opportunities presented by deep learning is its ability to capture complex, non-linear relationships in multi-omics data. The paper emphasizes the importance of interpretability and explainability in deep learning models applied to bioinformatics, as they play a crucial role in gaining biological insights and facilitating clinical decision-making. The integration of domain knowledge and biological context is highlighted as a critical aspect of model development. The paper showcases real-world applications of deep learning in multi-omics data integration, such as disease subtype classification, biomarker discovery, and drug response prediction. As the field continues to evolve, addressing these challenges and harnessing the potential of deep learning approaches will pave the way for transformative advancements in our understanding of complex biological systems and the development of precision medicine strategies.