ArticlePLoS computational biology2023
Predicting functional effects of ion channel variants using new phenotypic machine learning methods.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed.
- Drug-Refractory Dystonia: Benefits of Lamotrigine Therapy in a Case of SCN2A Epilepsy-Dyskinesia Syndrome.Movement disorders clinical practice · 2026Article
- Epilepsy-Associated Variants of a Single SCN1A Codon Exhibit Divergent Functional Properties.Annals of clinical and translational neurology · 2026Article
- Gene Portals: A Framework for Integrating Clinical, Functional, and Structural Evidence into Rare Disease Variant Classification.medRxiv : the preprint server for health sciences · 2026Article
- Progress in the study of ion channel function, mechanisms, and mathematical modeling in Parkinson's disease.iScience · 2026Review
- Reply to Letter to the Editor regarding the article "SCN1A gain of function effects in Dravet syndrome: Insights into clinical phenotypes and therapeutic implications".Epilepsia open · 2025Article
- To the editors: SCN1A gain-of-function effects in Dravet syndrome: Insights into clinical phenotypes and therapeutic implications.Epilepsia open · 2025Article
- Genotype-function-phenotype correlations for SCN1A variants identified by clinical genetic testing.Annals of clinical and translational neurology · 2025Article
- Learning molecular traits of human pain disease via voltage-gated sodium channel structure renormalization.Computational and structural biotechnology journal · 2025Article
- Mapping structural distribution and gating-property impacts of disease-associated mutations in voltage-gated sodium channels.iScience · 2024Article
- Machine learning models reveal distinct disease subgroups and improve diagnostic and prognostic accuracy for individuals with pathogenic SCN8A gain-of-function variants.Biology open · 2024Article
- Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.BMC genomics · 2023Article
- Review
- The role of ion channels in T cell function and disease.Frontiers in immunology · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Missense variants in genes encoding ion channels are associated with a spectrum of severe diseases. Variant effects on biophysical function correlate with clinical features and can be categorized as gain- or loss-of-function. This information enables a timely diagnosis, facilitates precision therapy, and guides prognosis. Functional characterization presents a bottleneck in translational medicine. Machine learning models may be able to rapidly generate supporting evidence by predicting variant functional effects. Here, we describe a multi-task multi-kernel learning framework capable of harmonizing functional results and structural information with clinical phenotypes. This novel approach extends the human phenotype ontology towards kernel-based supervised machine learning. Our gain- or loss-of-function classifier achieves high performance (mean accuracy 0.853 SD 0.016, mean AU-ROC 0.912 SD 0.025), outperforming both conventional baseline and state-of-the-art methods. Performance is robust across different phenotypic similarity measures and largely insensitive to phenotypic noise or sparsity. Localized multi-kernel learning offered biological insight and interpretability by highlighting channels with implicit genotype-phenotype correlations or latent task similarity for downstream analysis.
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Registered trials
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.