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

arXiv: https://arxiv.org/abs/2312.06082 · HTML: https://mlsanity.com/paper/2312.06082/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 89 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks](https://mlsanity.com/paper/1806.07537/) (2018)
- [Deep Learning in Robotics: A Review of Recent Research](https://mlsanity.com/paper/1707.07217/) (2017)
- [Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments](https://mlsanity.com/paper/1705.10874/) (2017)
- [An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery](https://mlsanity.com/paper/2008.00238/) (2020)
- [XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data](https://mlsanity.com/paper/2105.12807/) (2021)
- [Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts](https://mlsanity.com/paper/1912.12553/) (2019)
- [OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data](https://mlsanity.com/paper/1909.04169/) (2019)

## Built on by (most cited first, 2 of 2)

- [Debiasing Machine Unlearning with Counterfactual Examples](https://mlsanity.com/paper/2404.15760/) (2024)
- [Resolving superposition in AI for interpretability and cross-modal alignment in patient-neuronal images](https://mlsanity.com/paper/2606.31394/) (2026)

## Abstract (arXiv)

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.