ExplainabilityTransparencyTrustworthinessq-bio.GN
Explainable AI Methods for Multi-Omics Analysis: A Survey
Ahmad Hussein, Mukesh Prasad, Ali Braytee
October 15, 2024
arXiv.org
9 citations
Key Takeaways
-
Black box models pose clinical risks: Deep learning models used in multi-omics analysis lack transparency in their decision-making processes, which creates a significant barrier to clinical adoption since doctors need to understand why a model makes a particular diagnosis or treatment recommendation.
-
Explainable AI is essential for clinical translation: The paper emphasizes that xAI methods are "crucial" for making complex biological models interpretable to clinicians, suggesting that without transparency tools, valuable research insights may remain unusable in real healthcare settings.
-
Multi-omics data requires new interpretability approaches: Because multi-omics analysis integrates multiple biological data layers (genomics, proteomics, metabolomics, etc.) with nonlinear relationships, standard interpretability methods may be insufficient—specialized xAI techniques are needed to handle this complexity responsibly.
-
Transparency enables better clinical decision-making: The paper argues that xAI can provide clinicians with "clear insights" into model reasoning, which is foundational for responsible AI deployment in healthcare where decisions directly affect patient outcomes and clinicians must maintain accountability.
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.