ArticleScientific reports2023
Calculation of exact Shapley values for explaining support vector machine models using the radial basis function kernel.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Chemically Interpretable Explanations for Molecular Property Prediction via Fragment-Level Shapley Values.Journal of chemical information and modeling · 2026Article
- Revisiting the LSER Approach in the Era of Machine Learning: Insights from IAM Chromatography.Molecular pharmaceutics · 2026Article
- Diagnostic Model Development for IC/BPS and Its Subtypes Using Clinical Indicators, Urinary Biomarkers, and Single-Cell Transcriptomic Analysis.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- A two-stage hierarchical support vector machine framework detects roasted coffee adulterants through principal component analysis of hyperspectral imaging data.Scientific reports · 2026Article
- Integrating handcrafted and deep learning MRI signatures: an interpretable framework for predicting chemotherapy benefit in glioma.NPJ precision oncology · 2026Article
- Framework for evaluating explainable AI in antimicrobial drug discovery.Journal of cheminformatics · 2026Article
- High-Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Reducing Educational Bias in Cognitive Assessment via Dynamic Support Vector Machine Weighting: Validation Study on an Education-Stratified Dataset.JMIR rehabilitation and assistive technologies · 2026Article
- A hybrid unsupervised methodology on artificial intelligence filtering for automatically processing cellular DNA-encoded library (DEL) datasets.Bioinformatics (Oxford, England) · 2026Article
- Development and validation of an interpretable machine learning model for predicting 5-year recurrence in breast cancer.Frontiers in medicine · 2026Article
- Artificial intelligence-driven multivariate integration for pulmonary arterial pressure prediction in pulmonary hypertension.NPJ digital medicine · 2025Article
- Improving Machine Learning Classification Predictions through SHAP and Features Analysis Interpretation.Journal of chemical information and modeling · 2025Article
- Article
- Comparing Explanations of Molecular Machine Learning Models Generated with Different Methods for the Calculation of Shapley Values.Molecular informatics · 2025Article
- Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic Approach.Journal of hepatocellular carcinoma · 2025Article
- What can attribution methods show us about chemical language models?Digital discovery · 2024Article
- Improving the explainability of autoencoder factors for commodities through forecast-based Shapley values.Scientific reports · 2024Article
- Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping.Entropy (Basel, Switzerland) · 2024Article
- Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning.Briefings in bioinformatics · 2024Article
- TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Machine learning (ML) algorithms are extensively used in pharmaceutical research. Most ML models have black-box character, thus preventing the interpretation of predictions. However, rationalizing model decisions is of critical importance if predictions should aid in experimental design. Accordingly, in interdisciplinary research, there is growing interest in explaining ML models. Methods devised for this purpose are a part of the explainable artificial intelligence (XAI) spectrum of approaches. In XAI, the Shapley value concept originating from cooperative game theory has become popular for identifying features determining predictions. The Shapley value concept has been adapted as a model-agnostic approach for explaining predictions. Since the computational time required for Shapley value calculations scales exponentially with the number of features used, local approximations such as Shapley additive explanations (SHAP) are usually required in ML. The support vector machine (SVM) algorithm is one of the most popular ML methods in pharmaceutical research and beyond. SVM models are often explained using SHAP. However, there is only limited correlation between SHAP and exact Shapley values, as previously demonstrated for SVM calculations using the Tanimoto kernel, which limits SVM model explanation. Since the Tanimoto kernel is a special kernel function mostly applied for assessing chemical similarity, we have developed the Shapley value-expressed radial basis function (SVERAD), a computationally efficient approach for the calculation of exact Shapley values for SVM models based upon radial basis function kernels that are widely applied in different areas. SVERAD is shown to produce meaningful explanations of SVM predictions.
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Registered trials
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