ReviewComputational and structural biotechnology journal2025
Demystifying the black box: A survey on explainable artificial intelligence (XAI) in bioinformatics.
Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global research trends in gut microbiota and cellular senescence: a bibliometric and visual analysis from 2015 to 2025.Frontiers in microbiology · 2025Pooled it
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design.NAR genomics and bioinformatics · 2026Review
- A trifluoromethyl quinazoline inhibits hepatocellular carcinoma proliferation through HDAC1 binding and multi-target mechanisms.iScience · 2026Article
- Causality analysis of toxicological mechanisms in networked systems such as adverse outcome pathway networks.Archives of toxicology · 2026Review
- Explainable convolutional neural network model provides an alternative genome-wide association perspective on mutations in SARS-CoV-2.Scientific reports · 2026Article
- An explainable AI framework integrating machine and deep learning models for multi-species DNA functional group classification.Scientific reports · 2026Article
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- Development and validation of a generalisable machine learning algorithm for identifying interstitial lung disease cohorts: a retrospective cohort study.EClinicalMedicine · 2026Article
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- CLAMP: predicting specific protein-mediated chromatin loops in diverse species with a chromatin accessibility language model.Genome biology · 2026Article
- AI in biocuration: challenges, opportunities, and a roadmap for sustainable integration.Bioinformatics advances · 2026Article
- XAI in Genomics with LLM Generated Explanations in Oncology.CEUR workshop proceedings. · 2026Article
- The Era of Large-Scale Data in Biological Sciences.Advances in experimental medicine and biology · 2026Review
- Systems biology in the era of AI: "winter" or "evolution"?Frontiers in systems biology · 2026Article
- Marketing analytics in banking 4.0: A two-stage explainable AI framework for high-accuracy and well-calibrated predictions.PloS one · 2026Article
- SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation.Computational and structural biotechnology journal · 2026Article
- Assessing and optimizing Team-Based Learning in undergraduate cosmetic dermatology education: an empirical study using interpretable machine learning.BMC medical education · 2025Article
- Gastrointestinal Lesion Detection Using Ensemble Deep Learning Through Global Contextual Information.Bioengineering (Basel, Switzerland) · 2025Article
- Pseudo datasets estimate feature attribution in artificial neural networks.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The widespread adoption of Artificial Intelligence (AI) and machine learning (ML) tools across various domains has showcased their remarkable capabilities and performance. Black-box AI models raise concerns about decision transparency and user confidence. Therefore, explainable AI (XAI) and explainability techniques have rapidly emerged in recent years. This paper aims to review existing works on explainability techniques in bioinformatics, with a particular focus on omics and imaging. We seek to analyze the growing demand for XAI in bioinformatics, identify current XAI approaches, and highlight their limitations. Our survey emphasizes the specific needs of both bioinformatics applications and users when developing XAI methods and we particularly focus on omics and imaging data. Our analysis reveals a significant demand for XAI in bioinformatics, driven by the need for transparency and user confidence in decision-making processes. At the end of the survey, we provided practical guidelines for system developers.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.