  Artificial intelligence (AI), particularly machine learning and deep learning
models, has significantly impacted bioinformatics research by offering powerful
tools for analyzing complex biological data. However, the lack of
interpretability and transparency of these models presents challenges in
leveraging these models for deeper biological insights and for generating
testable hypotheses. Explainable AI (XAI) has emerged as a promising solution
to enhance the transparency and interpretability of AI models in
bioinformatics. This review provides a comprehensive analysis of various XAI
techniques and their applications across various bioinformatics domains
including DNA, RNA, and protein sequence analysis, structural analysis, gene
expression and genome analysis, and bioimaging analysis. We introduce the most
pertinent machine learning and XAI methods, then discuss their diverse
applications and address the current limitations of available XAI tools. By
offering insights into XAI's potential and challenges, this review aims to
facilitate its practical implementation in bioinformatics research and help
researchers navigate the landscape of XAI tools.
