Explainable AI Methods for Multi-Omics Analysis: A Survey

TL;DR

This paper reviews how explainable AI methods can enhance the interpretability of deep learning models used in multi-omics data analysis, aiding clinical understanding and application.

Contribution

It provides a comprehensive overview of xAI techniques tailored for multi-omics analysis, emphasizing their importance in making complex models transparent for clinical use.

Findings

xAI improves interpretability of multi-omics models

Enhanced transparency aids clinical decision-making

Survey highlights current xAI methods and challenges

Abstract

Advancements in high-throughput technologies have led to a shift from traditional hypothesis-driven methodologies to data-driven approaches. Multi-omics refers to the integrative analysis of data derived from multiple 'omes', such as genomics, proteomics, transcriptomics, metabolomics, and microbiomics. This approach enables a comprehensive understanding of biological systems by capturing different layers of biological information. Deep learning methods are increasingly utilized to integrate multi-omics data, offering insights into molecular interactions and enhancing research into complex diseases. However, these models, with their numerous interconnected layers and nonlinear relationships, often function as black boxes, lacking transparency in decision-making processes. To overcome this challenge, explainable artificial intelligence (xAI) methods are crucial for creating transparent models that allow clinicians to interpret and work with complex data more effectively. This review explores how xAI can improve the interpretability of deep learning models in multi-omics research, highlighting its potential to provide clinicians with clear insights, thereby facilitating the effective application of such models in clinical settings.