ArticleScientific reports2020
Computational pipeline to probe NaV1.7 gain-of-function variants in neuropathic painful syndromes.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed, 12 citations in OpenAlex.
- Collision-free morgan fingerprints: a principled approach to enhance machine learning performance and interpretability in chemistry.Journal of cheminformatics · 2026Article
- Learning molecular traits of human pain disease via voltage-gated sodium channel structure renormalization.Computational and structural biotechnology journal · 2025Article
- Rinmaker: a fast, versatile and reliable tool to determine residue interaction networks in proteins.BMC bioinformatics · 2023Article
- VariBench, new variation benchmark categories and data sets.Frontiers in bioinformatics · 2023Article
- Hydropathicity-based prediction of pain-causing NaV1.7 variants.BMC bioinformatics · 2021Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 4 countries.
Funding
Abstract
Applications of machine learning and graph theory techniques to neuroscience have witnessed an increased interest in the last decade due to the large data availability and unprecedented technology developments. Their employment to investigate the effect of mutational changes in genes encoding for proteins modulating the membrane of excitable cells, whose biological correlates are assessed at electrophysiological level, could provide useful predictive clues. We apply this concept to the analysis of variants in sodium channel NaV1.7 subunit found in patients with chronic painful syndromes, by the implementation of a dedicated computational pipeline empowering different and complementary techniques including homology modeling, network theory, and machine learning. By testing three templates of different origin and sequence identities, we provide an optimal condition for its use. Our findings reveal the usefulness of our computational pipeline in supporting the selection of candidates for cell electrophysiology assay and with potential clinical applications.
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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.