ArticleBiomolecules2022
MARGINAL: An Automatic Classification of Variants in
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Disease- and gene-specific deep learning for pathogenicity prediction of rare missense variants in cancer predisposition genes.BioData mining · 2026Article
- Comprehensive evaluation of ACMG/AMP-based variant classification tools.Bioinformatics (Oxford, England) · 2026Article
- A machine learning framework for predictive interpretation of variants of uncertain significance in hereditary cancer.Frontiers in systems biology · 2026Article
- Artificial intelligence in cancer genomics: refining diagnosis of hereditary breast and ovarian cancer syndromes.Annals of medicine and surgery (2012) · 2026Article
- Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities.Human genomics · 2025Review
- Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Implementation of next-generation sequencing (NGS) for the genetic analysis of hereditary diseases has resulted in a vast number of genetic variants identified daily, leading to inadequate variant interpretation and, consequently, a lack of useful clinical information for treatment decisions. Herein, we present MARGINAL 1.0.0, a machine learning (ML)-based software for the interpretation of rare
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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.