XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications

AI models in biology need to be understandable, not just accurate. While machine learning and deep learning have become powerful tools for analyzing biological data, their "black box" nature makes it difficult for researchers to understand why the models make specific predictions or to generate new biological hypotheses based on those predictions.

Explainable AI (XAI) is emerging as a practical solution across multiple biological domains. The paper identifies XAI applications spanning DNA, RNA, and protein sequence analysis, structural analysis, gene expression, genome analysis, and bioimaging—demonstrating that interpretability techniques can be adapted across diverse areas of bioinformatics research.

Transparency in AI-driven biology supports scientific validation and trust. By making AI models more interpretable, researchers can better validate whether the biological insights generated by these models are scientifically sound and can develop testable hypotheses, rather than simply accepting predictions without understanding the underlying reasoning.

Current XAI tools have significant limitations that researchers need to navigate. The paper acknowledges that available XAI tools face challenges in practical implementation, suggesting that researchers working with AI in bioinformatics need guidance on selecting and applying these techniques appropriately to their specific research questions.