ArticlePloS one2018
ENTPRISE-X: Predicting disease-associated frameshift and nonsense mutations.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- MENDELSEEK: An algorithm that predicts mendelian genes and elucidates what makes them special.PLoS computational biology · 2026Article
- A novel truncated mutation in folate receptor α (FRα) affecting its glycosylation and affinity for folate in a consanguineous family with progressive encephalopathy: follow up and treatment improvement.Molecular biology reports · 2025Article
- Retinal Dystrophies Associated With Peripherin-2: Genetic Spectrum and Novel Clinical Observations in 241 Patients.Investigative ophthalmology & visual science · 2024Article
- A novel homozygous mutation in TRAPPC9 gene causing autosomal recessive non-syndromic intellectual disability.BMC medical genomics · 2022Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- LeMeDISCO is a computational method for large-scale prediction & molecular interpretation of disease comorbidity.Communications biology · 2022Article
- Clinical heterogeneity of hyperornithinemia-hyperammonemia-homocitrullinuria syndrome in thirteen palestinian patients and report of a novel variant in theFrontiers in genetics · 2022Article
- Novel imaging and clinical phenotypes of CONDSIAS disorder caused by a homozygous frameshift variant of ADPRHL2: a case report.BMC neurology · 2020Review
- Three Novel Variants identified in FBN1 and TGFBR2 in seven Iranian families with suspected Marfan syndrome.Molecular genetics & genomic medicine · 2020Article
- Detection of novel mitochondrial mutations in cytochrome C oxidase subunit 1 (COX1) in patients with familial adenomatous polyposis (FAP).Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2020Article
- MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action.Molecular pharmaceutics · 2020Article
- Differential kinase activity of ACVR1 G328V and R206H mutations with implications to possible TβRI cross-talk in diffuse intrinsic pontine glioma.Scientific reports · 2020Article
- Whole-exome sequencing identified a novel variant in an Iranian patient affected by pycnodysostosis.Molecular genetics & genomic medicine · 2020Article
- Variation benchmark datasets: update, criteria, quality and applications.Database : the journal of biological databases and curation · 2020Article
- Association of Pathogenic Missense and Nonsense Mutations in Mitochondrial COII Gene with Familial Adenomatous Polyposis (FAP).International journal of molecular and cellular medicine · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
To exploit the plethora of information provided by Next Generation Sequencing, the identification of the genetic mutations responsible for disease in general or cancer in particular, among the thousands of neutral germline or somatic variations is a crucial task. Genome-wide association studies for the detection of disease-associated genes or cancer drivers can only identify common variations or driver genes in a cohort of patients. Thus, they cannot discover unique disease-associated mutations or cancer driver genes on a personal basis. Moreover, even when there are such common variations, their significance is unknown. Here, we extend the machine learning based approach ENTPRISE developed for predicting the disease association of missense mutations to frameshift and nonsense mutations. The new approach, ENTPRISE-X, is shown to outperform the state-of-the-art methods VEST-indel and DDIG-in for predicting the disease association of germline frameshift mutations in terms of balanced measure Matthew's correlation coefficient, MCC, with a MCC of 0.586 for ENTPRISE-X, versus 0.412 by VEST-indel and 0.321 by DDIG-in, respectively. Large scale testing on the ExAC dataset shows ENTPRISE-X has a much lower fraction of 16% of variations classified as disease causing, as compared to VEST-indel's 26% and DDIG-in's 65% of predictions as being disease-associated. A web server for ENTPRISE-X is freely available for academic users at http://cssb2.biology.gatech.edu/entprise-x.
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