Paper List
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PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer
This paper addresses the dual challenge of achieving computational efficiency without sacrificing accuracy in whole-transcriptome single-cell represen...
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Beyond Bayesian Inference: The Correlation Integral Likelihood Framework and Gradient Flow Methods for Deterministic Sampling
This paper addresses the core challenge of calibrating complex biological models (e.g., PDEs, agent-based models) with incomplete, noisy, or heterogen...
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Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
This paper addresses the core challenge of detecting viral variants in wastewater sequencing data without reference genomes or labeled annotations, ov...
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited his...
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Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring ...
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Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study
This study addresses the critical gap in understanding heterogeneous contact patterns within aged care facilities, where existing population-level con...
DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
Yale University | Microsoft
The 30-Second View
IN SHORT: This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prioritizes structural motifs over functional residues, complicating reliable identification of drug targets.
Innovation (TL;DR)
- Methodology Comprehensive benchmarking of three post-hoc explainability methods (GradCAM, Excitation Backpropagation, PGExplainer) on DeepFRI with quantitative sparsity analysis.
- Methodology Development of a modified DeepFool adversarial testing framework for protein sequences, measuring mutation thresholds required for misclassification.
- Biology Revealed that DeepFRI prioritizes amino acids controlling protein structure over function in >50% of tested proteins, highlighting a fundamental accuracy-interpretability trade-off.
Key conclusions
- DeepFRI required 206 mutations (62.4% of 330 residues) in the lac repressor for misclassification, demonstrating extreme robustness but potentially missing subtle functional alterations.
- Explainability methods showed significant granularity differences: PGExplainer was 3× sparser than GradCAM and 17× sparser than Excitation Backpropagation across 124 binding proteins.
- All three methods converged on biochemically critical P-loop residues (0-20) in ARF6 GTPase, validating DeepFRI's focus on conserved functional motifs in straightforward domains.
Abstract: Machine learning technologies for protein function prediction are black box models. Despite their potential to identify key drug targets with high accuracy and accelerate therapy development, the adoption of these methods depends on verifying their findings. This study evaluates DeepFRI, a leading Graph Convolutional Network (GCN)-based tool, using advanced explainability techniques—GradCAM, Excitation Backpropagation, and PGExplainer—and adversarial robustness tests. Our findings reveal that the model’s predictions often prioritize conserved motifs over truly deterministic residues, complicating the identification of functional sites. Quantitative analyses show that explainability methods differ significantly in granularity, with GradCAM providing broad relevance and PGExplainer pinpointing specific active sites. These results highlight trade-offs between accuracy and interpretability, suggesting areas for improvement in DeepFRI’s architecture to enhance its trustworthiness in drug discovery and regulatory settings.