Explainable artificial intelligence for multi-omics data

Area of Science:

• Biomedical Informatics
• Computational Biology
• Artificial Intelligence

Background:

• Multi-omics data integration is advancing biomedical predictions using machine learning (ML).
• High-dimensional omics data presents challenges in model interpretability.
• Next-generation sequencing and computing technologies enable complex data analysis.

Purpose of the Study:

• To discuss eXplainable Artificial Intelligence (XAI) algorithms and models for multi-omics biomedical predictions.
• To highlight the importance of interpretability in ML models for healthcare.
• To demonstrate how XAI enhances transparency and trustworthiness in multi-omics research.

Main Methods:

• Review of XAI algorithms applicable to multi-omics data.
• Discussion of XAI models for enhancing ML interpretability.
• Integration of multi-omics data for comprehensive biomedical insights.

Main Results:

• XAI approaches can effectively address the lack of interpretability in ML models.
• Multi-omics data provides a holistic view of biological processes.
• XAI models improve the transparency and trustworthiness of biomedical predictions.

Conclusions:

• Multi-omics XAI models are vital for accurate and interpretable biomedical predictions.
• XAI is crucial for understanding complex patterns in multi-omics data.
• Integrating multi-omics and XAI fosters trust and clinical utility in AI-driven healthcare.