Artificial intelligence (AI) systems utilizing deep neural networks and machine learning (ML) algorithms are widely used for solving critical problems in bioinformatics, biomedical informatics and precision medicine. However, complex ML models that are often perceived as opaque and black-box methods 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. In sensitive areas such as healthcare, explainability and accountability are not only desirable properties 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 AI (XAI) aims to overcome the opaqueness of black-box models and to provide transparency in how AI systems make decisions. Interpretable ML models can explain how they make predictions and identify factors that influence their outcomes. However, the majority of the state-of-the-art interpretable ML methods are domain-agnostic and have evolved from fields such as computer vision, automated reasoning or statistics, making direct application to bioinformatics problems challenging without customization and domain adaptation. In this paper, we discuss the importance of explainability and algorithmic transparency in the context of bioinformatics. We provide an overview of model-specific and model-agnostic interpretable ML methods and tools and outline their potential limitations. We discuss how existing interpretable ML methods can be customized and fit to bioinformatics research problems. Further, through case studies in bioimaging, cancer genomics and text mining, we demonstrate how XAI methods can improve transparency and decision fairness. Our review aims at providing valuable insights and serving as a starting point for researchers wanting to enhance explainability and decision transparency while solving bioinformatics problems. GitHub: https://github.com/rezacsedu/XAI-for-bioinformatics.

The increasing variety of Linked Data on the Web makes it challenging to determine the quality of this data and, subsequently, to make this information explicit to data consumers. Despite the availability of a number of tools and frameworks to assess Linked Data Quality, the output of such tools is not suitable for machine consumption, and thus consumers can hardly compare and rank datasets in the order of fitness for use . This article describes a conceptual methodology for assessing Linked Datasets, and Luzzu; a framework for Linked Data Quality Assessment. Luzzu is based on four major components: (1) an extensible interface for defining new quality metrics; (2) an interoperable , ontology-driven back-end for representing quality metadata and quality problems that can be re-used within different semantic frameworks; (3) scalable dataset processors for data dumps, SPARQL endpoints, and big data infrastructures; and (4) a customisable ranking algorithm taking into account user-defined weights. We show that Luzzu scales linearly against the number of triples in a dataset. We also demonstrate the applicability of the Luzzu framework by evaluating and analysing a number of statistical datasets against a variety of metrics. This article contributes towards the definition of a holistic data quality lifecycle, in terms of the co-evolution of linked datasets, with the final aim of improving their quality.

The increasing adoption of the Linked Data principles brought with it an unprecedented dimension to the Web, transforming the traditional Web of Documents to a vibrant information ecosystem, also known as the Web of Data. This transformation, however

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