Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
Yale University | Microsoft
30秒速读
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.
核心创新
- 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.
主要结论
- 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.
摘要: 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.