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Review
. 2023 Nov 22;25(1):bbad453.
doi: 10.1093/bib/bbad453.

Explainable artificial intelligence for omics data: a systematic mapping study

Philipp A Toussaint  1   2 , Florian Leiser  1 , Scott Thiebes  1 , Matthias Schlesner  3 , Benedikt Brors  4   5 , Ali Sunyaev  1
Affiliations
Review

Explainable artificial intelligence for omics data: a systematic mapping study

Philipp A Toussaint et al. Brief Bioinform. .

Abstract

Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in their respective research community. An overview of XAI for omics data is needed to highlight promising approaches and help detect common issues. Toward this end, we conducted a systematic mapping study. To identify relevant literature, we queried Scopus, PubMed, Web of Science, BioRxiv, MedRxiv and arXiv. Based on keywording, we developed a coding scheme with 10 facets regarding the studies' AI methods, explainability methods and omics data. Our mapping study resulted in 405 included papers published between 2010 and 2023. The inspected papers analyze DNA-based (mostly genomic), transcriptomic, proteomic or metabolomic data by means of neural networks, tree-based methods, statistical methods and further AI methods. The preferred post-hoc explainability methods are feature relevance (n = 166) and visual explanation (n = 52), while papers using interpretable approaches often resort to the use of transparent models (n = 83) or architecture modifications (n = 72). With many research gaps still apparent for XAI for omics data, we deduced eight research directions and discuss their potential for the field. We also provide exemplary research questions for each direction. Many problems with the adoption of XAI for omics data in clinical practice are yet to be resolved. This systematic mapping study outlines extant research on the topic and provides research directions for researchers and practitioners.

Keywords: biomedical data; explainable artificial intelligence; interpretable artificial intelligence; machine learning; omics; systematic mapping study.

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Figures

Figure 1
Figure 1
Overview of the systematic mapping process adapted from Petersen [48].
Figure 2
Figure 2
PRISMA flow diagram of the study selection process.
Figure 3
Figure 3
Number and document type of publications by year.
Figure 4
Figure 4
Top five outlets (A) and scientific fields (B) of included publications.
Figure 5
Figure 5
Distribution of explainability method (A), AI task (B), medical field (C) and omics data (D) of included publications.
Figure 6
Figure 6
Distribution of XAI models found in our literature set.
Figure 7
Figure 7
Bubble chart of applied AI method and used omics data.
Figure 8
Figure 8
Bubble chart of applied AI method and explainability method.
Figure 9
Figure 9
Alluvial chart mapping omics data on AI method and explainability method.
See this image and copyright information in PMC

References

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