ArticlePLoS computational biology2022
Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- DeepAden: an explainable machine learning method for predicting the substrate specificity of nonribosomal peptide synthetases.Nucleic acids research · 2026Article
- Development and prospective evaluation of a real-time deep learning model for inpatient hypoglycemia prediction.NPJ digital medicine · 2026Article
- Artificial intelligence in the diagnosis and prognosis of pediatric bacterial pneumonia: current advances and challenges.Current opinion in pediatrics · 2026Review
- Network toxicology integrated with machine learning and SHAP analysis identifies overlapping immune signatures between Di(2-ethylhexyl) phthalate (DEHP) and Sjögren's syndrome.BMC pharmacology & toxicology · 2026Article
- Optimizing genomic language models for promoter prediction: a comparative study of tokenization and cross-species learning.NAR genomics and bioinformatics · 2026Article
- Group-based trajectory modelling and interpretable machine learning to identify factors associated with neurodevelopmental trajectories in preterm infants.BMC pediatrics · 2026Article
- PLM-eXplain: divide and conquer the protein embedding space.Bioinformatics (Oxford, England) · 2026Article
- Machine learning model for predicting hypotension following continuous renal replacement therapy initiation in end-stage kidney disease patients: a SHAP-interpretable approach.Frontiers in medicine · 2026Article
- Article
- Machine learning model predicts clotting risk during CRRT in ESKD patients: a SHAP-interpretable approach.Renal failure · 2025Article
- Utility of Deep Learning to Address Missing Modalities from Multi-Modal Medical Imaging Studies: A Systematic Review.Artificial intelligence and applications (Commerce, Calif.) · 2025Article
- Prediction of enzyme function using an interpretable optimized ensemble learning framework.Chemical science · 2025Article
- Machine learning algorithms to predict the risk of hyperlipidemia in people with HIV after starting HAART for 6 months.AIDS (London, England) · 2025Article
- Review
- ESM2_AMP: an interpretable framework for protein-protein interactions prediction and biological mechanism discovery.Briefings in bioinformatics · 2025Article
- Charting γ-secretase substrates by explainable AI.Nature communications · 2025Article
- Prediction of protein subcellular localization in single cells.Nature methods · 2025Article
- Development and validation of a hypoxemia prediction model in middle-aged and elderly outpatients undergoing painless gastroscopy.Scientific reports · 2025Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- Assessing perioperative risks in a mixed elderly surgical population using machine learning: A multi-objective symbolic regression approach to cardiorespiratory fitness derived from cardiopulmonary exercise testing.PLOS digital health · 2025Article
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2 authors.
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Abstract
Machine learning with multi-layered artificial neural networks, also known as "deep learning," is effective for making biological predictions. However, model interpretation is challenging, especially for sequential input data used with recurrent neural network architectures. Here, we introduce a framework called "Positional SHAP" (PoSHAP) to interpret models trained from biological sequences by utilizing SHapely Additive exPlanations (SHAP) to generate positional model interpretations. We demonstrate this using three long short-term memory (LSTM) regression models that predict peptide properties, including binding affinity to major histocompatibility complexes (MHC), and collisional cross section (CCS) measured by ion mobility spectrometry. Interpretation of these models with PoSHAP reproduced MHC class I (rhesus macaque Mamu-A1*001 and human A*11:01) peptide binding motifs, reflected known properties of peptide CCS, and provided new insights into interpositional dependencies of amino acid interactions. PoSHAP should have widespread utility for interpreting a variety of models trained from biological sequences.
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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.