LEAP: Using machine learning to support variant classification in a clinical setting-Reference-Cited by-同舟云学术

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LEAP: Using machine learning to support variant classification in a clinical setting

Published:2020-04 Issue:6 Volume:41 Page:1079-1090
ISSN:1059-7794
Container-title:Human Mutation
language:en
Short-container-title:Human Mutation

Author:

Lai Carmen¹,Zimmer Anjali D.²^ORCID,O'Connor Robert³,Kim Serra³,Chan Ray³,Akker Jeroen⁴,Zhou Alicia Y.²,Topper Scott⁵,Mishne Gilad¹

Affiliation:

1. Data ScienceColor Genomics Burlingame California

2. Scientific AffairsColor Genomics Burlingame California

3. Variant ScienceColor Genomics Burlingame California

4. BioinformaticsColor Genomics Burlingame California

5. Clinical GenomicsColor Genomics Burlingame California

Funder

National Institutes of Health

Publisher

Wiley

Subject

Genetics (clinical),Genetics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/humu.24011

Reference21 articles.

1. Predicting functional effect of human missense mutations using PolyPhen‐2;Adzhubei I.;Current Protocols in Human Genetics,2013

2. ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants

3. Identification of deleterious mutations within three human genomes

4. Assessment of blind predictions of the clinical significance of BRCA1 and BRCA2 variants

5. Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++

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1. Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors;Human Genomics;2024-08-28

2. CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods;Genome Biology;2024-02-22

3. Unraveling Complexities in Genetically Elusive Long QT Syndrome;Circulation: Arrhythmia and Electrophysiology;2024-02

4. Artificial intelligence in cardiovascular genetics;Intelligence-Based Cardiology and Cardiac Surgery;2024

5. A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer;Briefings in Bioinformatics;2023-11-22

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