  Artificial intelligence (AI) systems utilizing deep neural networks (DNNs)
and machine learning (ML) algorithms are widely used for solving important
problems in bioinformatics, biomedical informatics, and precision medicine.
However, complex DNNs or ML models, which are often perceived as opaque and
black-box, can make it difficult to understand the reasoning behind their
decisions. This lack of transparency can be a challenge for both end-users and
decision-makers, as well as AI developers. Additionally, in sensitive areas
like healthcare, explainability and accountability are not only desirable but
also legally required for AI systems that can have a significant impact on
human lives. Fairness is another growing concern, as algorithmic decisions
should not show bias or discrimination towards certain groups or individuals
based on sensitive attributes. Explainable artificial intelligence (XAI) aims
to overcome the opaqueness of black-box models and provide transparency in how
AI systems make decisions. Interpretable ML models can explain how they make
predictions and the factors that influence their outcomes. However, most
state-of-the-art interpretable ML methods are domain-agnostic and evolved from
fields like computer vision, automated reasoning, or statistics, making direct
application to bioinformatics problems challenging without customization and
domain-specific adaptation. In this paper, we discuss the importance of
explainability in the context of bioinformatics, provide an overview of
model-specific and model-agnostic interpretable ML methods and tools, and
outline their potential caveats and drawbacks. Besides, we discuss how to
customize existing interpretable ML methods for bioinformatics problems.
Nevertheless, we demonstrate how XAI methods can improve transparency through
case studies in bioimaging, cancer genomics, and text mining.
