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.

Artificial intelligence (AI) systems are increasingly used in health and personalized care. However, the adoption of data-driven approaches in many clinical settings has been hampered due to their inability to perform in a reliable and safe manner to leverage accurate and trustworthy diagnoses. A critical and challenging usage scenario for AI is aiding the treatment of cancerous conditions. Providing accurate diagnosis for cancer is a challenging problem in precision oncology. Although machine learning (ML)-based approaches are very effective at cancer susceptibility prediction and subsequent treatment recommendations, ML models can be vulnerable to adversarial attacks. Since adversarially weak models can lead to wrong clinical recommendations, such vulnerabilities is more critical -especially when AI-guided systems are used to aid medical doctors. Therefore, it is indispensable that healthcare professionals employ trustworthy AI tools for predicting and assessing disease risks and progression. In this paper, we propose an adversaryaware multimodal convolutional autoencoder (MCAE) model for cancer susceptibility prediction from multi-omics data consisting of copy number variations (CNVs), miRNA expression, and gene expression (GE). Based on different representational learning techniques, the MCAE model learns multimodal feature representations from multi-omics data, followed by classifying the patient cohorts into different cancer types on multimodal embedding space that exhibit similar characteristics in end-to-end setting. To make the MCAE model robust to adversaries and to provide consistent diagnosis, we formulate robustness as a property, such that predictions remain stable with regard to small variations in the input. We study different adversarial attacks scenarios and take both proactive and reactive measures (e.g., adversarial retraining and identification of adversarial inputs). Experiment results show that the MCAE model based on latent representation concatenation (LRC) exhibits high confidence at predicting cancer types, giving an average precision and Matthews correlation coefficient (MCC) scores of 0.9625 and 0.8453, respectively and shows higher robustness when compared and tested with state-of-the-art approaches against different attack scenarios w.r.t. ERM and CLEVER scores. Overall, our study suggests that a well-fitted and adversarially robust model can provide consistent and reliable diagnosis for cancer.