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Explainable artificial intelligence for omics data: a systematic mapping study.

Philipp A Toussaint^1,2, Florian Leiser¹, Scott Thiebes¹

¹Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany.

Briefings in Bioinformatics

|December 19, 2023

Summary

This summary is machine-generated.

Explainable artificial intelligence (XAI) is crucial for analyzing omics data. This study maps XAI applications in omics, identifying trends, research gaps, and future directions for biological insights and clinical practice.

Keywords:

biomedical data explainable artificial intelligence interpretable artificial intelligence machine learning omics systematic mapping study

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Area of Science:

Bioinformatics
Artificial Intelligence
Genomics

Background:

Explainable artificial intelligence (XAI) is increasingly used for omics data analysis.
Interdisciplinary nature leads to fragmented findings, necessitating a consolidated overview.
A systematic mapping study is required to synthesize current research on XAI for omics.

Purpose of the Study:

To provide a comprehensive overview of XAI applications in omics data analysis.
To identify common AI and explainability methods used.
To highlight research gaps and suggest future research directions.

Main Methods:

Systematic mapping study of scientific literature.
Searched Scopus, PubMed, Web of Science, BioRxiv, MedRxiv, and arXiv.
Developed a coding scheme with 10 facets for AI methods, explainability, and omics data.

Main Results:

Included 405 papers published between 2010-2023.
Analyzed genomic, transcriptomic, proteomic, and metabolomic data using various AI methods.
Identified feature relevance and visual explanations as common post-hoc methods, and transparent models for interpretable approaches.

Conclusions:

Significant research gaps exist in XAI for omics data.
Eight research directions are proposed to advance the field.
Challenges remain for clinical adoption of XAI in omics research.