Explainable Artificial Intelligence (XAI)

Application of deep learning and explainable AI-supported medical decision-making for facial phenotyping in genetic syndromes

This study evaluates if explainable AI (XAI) improves diagnostic accuracy and confidence in genetic syndrome recognition using facial images, finding mixed results with AI predictions having greater impact than XAI explanations.

An explainable machine learning consensus framework for robust estimations of environmental effects on population dynamics

This paper develops a framework to assess explainable ML sensitivity by comparing consensus across model architectures, using synthetic coral data to demonstrate its utility in ecological modeling.

Counterfactual Explanations for Graph Neural Networks in Patient Outcome Prediction

This paper introduces a counterfactual explanation algorithm for graph neural networks to explain patient outcome predictions in patient similarity networks.

Improving Data Quality, Model Transparency and Performance in Lung Histopathology with Explainable AI

This paper applies explainable AI techniques to improve transparency, data quality, and performance in lung histopathology image classification.

Biomarker Fidelity Score - A Quantitative Framework for Individual-Level Validation of Explainability Methods in 3D Alzheimer's Disease MRI Classification

The paper introduces a quantitative framework to validate explainability methods in Alzheimer's disease MRI classification at the individual level using biomarker-aligned spatial overlap scores.

Efficient Game-Theoretic Explanations for Tree-Based Ensembles via Owen Values

This paper introduces TreeOwen, algorithms for computing Owen values in tree-based ensembles to provide structured, multi-resolution explanations for grouped features in scientific applications.

Trustworthy in silico labeling via semantic visual interpretability of image-to-image translation

The paper introduces Mask Interpreter, a method to improve interpretability of image-to-image translation models, validating biological relevance and identifying artifacts in in silico labeling.

GDTR: Layer-wise Settling Depth Reveals Biological Grammar in Genomic Foundation Models

GDTR measures nucleotide settling depth in genomic models to reveal biological grammar stability across layers, showing splice sites settle earlier than intronic regions and identifying variant impact through differential depths.

OmiXAI: An Ensemble XAI Pipeline for Interpretable Deep Learning in Omics Data

OmiXAI is an ensemble XAI pipeline for deep learning in omics data that reduces feature sets and benchmarks XAI methods on Z-DNA prediction.

Does ensembling improve feature attributions from sequence-to-activity models?

This study evaluates if ensembling improves feature attributions from sequence-to-activity models, finding that it enhances downstream applications like motif identification and variant prediction.

Benchmarking attention-based methods for vision transformers' interpretability in retinal fundus imaging

This study benchmarks attention-based interpretability methods for Vision Transformers in retinal fundus imaging, finding gradient-weighted attention rollout provides the most faithful and biologically relevant attributions.

Counterfactual Diffusion Models for Interpretable Explanations of Artificial Intelligence Models in Pathology

MoPaDi generates counterfactual explanations for histopathology images to reveal morphological features driving classifier predictions, improving interpretability in digital pathology.

VFUSE: Virulent Feature Understanding with Sparse autoEncoders

VFUSE uses sparse autoencoders to audit protein design models for hazardous features, improving interpretability without sacrificing performance.

Mechanistic Interpretability for Protein Language Models: A Validation Framework

This paper evaluates two mechanistic interpretability methods on a protein language model to identify features distinguishing Class A beta-lactamase from other classes.

Interpretable Predictive Modeling for Medical Data Using Boolean Rule-aware Regression

This paper introduces RBBR, a framework that generates interpretable Boolean rules for medical prediction, achieving strong performance and clinical relevance across six datasets.

A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images

This paper introduces MorphoXAI, a human-in-the-loop framework for explaining AI predictions on whole-slide images, showing it provides accurate and interpretable histomorphological explanations at both global and local levels.

The illusion of interpretability in biologically informed neural networks

This paper shows that biologically informed neural networks (BINNs) cannot reliably recover internal biological structures from outputs alone due to nonidentifiability.

Peering Inside the Black Box: Explainable AI to Interpret Advanced Computer Vision Fungal Pathogen Prediction

The paper trains AI models to identify fungal pathogens from microscopy images and uses explainable AI to reveal the features influencing predictions.

Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging

This paper develops a framework combining Oblique Random Forests and NEOFIT for sex classification in brain imaging across species, achieving high accuracy and interpretable feature importance.

TPCAV: Interpreting deep learning genomics models via concept attribution

This paper introduces TPCAV, a method to interpret genomics deep learning models by adapting concept attribution, showing it can analyze both motif features and general genomic concepts.

DVPNet: A New XAI-Based Interpretable Genetic Profiling Framework Using Nucleotide Transformer and Probabilistic Circuits

The paper introduces DVPNet, an XAI framework combining Nucleotide Transformer and probabilistic circuits to identify important genes in cancer classification using single-cell data.

What Do Biological Foundation Models Compute? Sparse Autoencoders from Feature Recovery to Mechanistic Interpretability

This paper reviews sparse autoencoders applied to biological foundation models, finding they recover interpretable biological features but lack experimental validation of mechanistic understanding.

How to gain valuable insight from scarce data with Machine Learning: a post-hoc explanation tool to identify biases in biological images classification

This study uses ML to classify regenerating vs. scarring mouse tissue images, finding models overfit to individual mice rather than biological features, but successfully extract relevant biological information when tasks align with data capabilities.

XAI-ecDNA: A Proof-of-Concept Framework for Explainable Multi-Modal Extrachromosomal DNA Detection Using Synthetic FISH and Genomics

This paper introduces XAI-ecDNA, a framework for detecting extrachromosomal DNA using synthetic FISH and genomic data, achieving high performance and interpretability.

The eXplainable Artificial Intelligence (XAI) Triad: Models, Importances, and Significance at Scale

This paper evaluates XAI methods for variable selection in biological data, finding that RF/Accuracy/PIMP performs best but is computationally intensive, while DT/Gini/simThresh offers a more scalable alternative.

Keeping SCORE enables interpretable uncertainty-aware classification from diffusion models for genomics

Keeping SCORE transforms diffusion models into probabilistic classifiers for genomics, providing accurate predictions with uncertainty and interpretability across multiple tasks.

Systematic evaluation of peptide property predictors with explainable AI technique SHAP

The paper evaluates peptide property predictors using SHAP to explain model predictions and finds strong correlations with known amino acid indices.

Decode-gLM: A toolkit for interpreting, auditing, and steering genomic language models

This paper introduces Decode-gLM, a toolkit using sparse autoencoders to interpret, audit, and steer genomic language models, revealing biological features and identifying data contamination.

A layer-resolved diagnostic identifies bias-driven decisions in deep neural networks

This paper introduces the Bias Dominance Index (BDI) to diagnose whether neural network decisions are feature-supported or bias-driven, showing that high confidence can coexist with bias-driven decisions.

A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues

The paper introduces a visualization method to analyze SHAP feature interactions in ML models for biomedical applications, revealing hidden patterns of explainability.