Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Predicting Ovarian/Breast Cancer Pathogenic Risks of Human BRCA1 Gene Variants of Unknown Significance.

BioMed research international - 1 Jan 2021

Lin Hui-Heng, Xu Hongyan, Hu Hongbo, Ma Zhanzhong, Zhou Jie, Liang Qingyun

Abstract excerpt

High-throughput sequencing is gaining popularity in clinical diagnoses, but more and more novel gene variants with unknown clinical significance are being found, giving difficulties to interpretations of people's genetic data, precise disease diagnoses, and the making of therapeutic strategies and decisions. In order to solve these issues, it is of critical importance to figure out ways to analyze and interpret...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to BRCA1 Protein
Share to Breast Neoplasms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Genetic

Share to Ovarian Neoplasms

Share to Risk Factors

Share to ROC Curve

Share to Support Vector Machine

Source

Open in PubMed Open full text

Identifiers

PMID: 33937409
DOI: 10.1155/2021/6667201
PMCID: PMC8062186

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Predicting Ovarian/Breast Cancer Pathogenic Risks of Human BRCA1 Gene Variants of Unknown Significance.PMID 33937409Read ↗

Predicting ovarian/breast cancer pathogenic risks of BRCA1 gene variants of unknown significanceSemantic proximity Read ↗

Machine learning approach to assess the pathogenicity of BRCA1/2 genetic variants : brca-NOVUSSemantic proximity Read ↗

BRCA1/2 variants of unknown significance in hereditary breast and ovarian cancer (HBOC) syndrome: Looking for the hidden meaning.Semantic proximity Read ↗

BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy.Semantic proximity Read ↗

Determination of Pathogenicity of Breast Cancer 1 Gene Variants using the American College of Medical Genetics and Genomics and the Association for Molecular Pathology Guidelines.Semantic proximity Read ↗

Cancer genomic profiling predicts pathogenicity of BRCA1 and BRCA2 variantsSemantic proximity Read ↗

Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variantsSemantic proximity Read ↗

Classification of variants of uncertain significance in BRCA1 and BRCA2 using personal and family history of cancer from individuals in a large hereditary cancer multigene panel testing cohort.Semantic proximity Read ↗

Germline Missense Variants in BRCA1: New Trends and Challenges for Clinical AnnotationSemantic proximity Read ↗

BRCA1/2 sequence variants of uncertain significance: a primer for providers to assist in discussions and in medical management.Semantic proximity Read ↗

Estimating risks for variants of unknown significance according to their predicted pathogenicity classes with application to BRCA1.Semantic proximity Read ↗

Understanding and predicting the functional consequences of missense mutations in BRCA1 and BRCA2.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.