AIMC Topic: Germ-Line Mutation

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Predicting mutational function using machine learning.

Mutation research. Reviews in mutation research Mar 23, 2023
Genetic variations are one of the major causes of phenotypic variations between human individuals. Although beneficial as being the substrate of evolution, germline mutations may cause diseases, including Mendelian diseases and complex diseases such ...
Germ-Line Mutation Neoplasms Proteins Mutation Humans Machine Learning
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Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer.

International journal of molecular sciences Sep 26, 2022
BRCA 1/2 genes mutation status can already determine the therapeutic algorithm of high grade serous ovarian cancer patients. Nevertheless, its assessment is not sufficient to identify all patients with genomic instability, since BRCA 1/2 mutations ar...
Humans Eosine Yellowish-(YS) Hematoxylin Ovarian Neoplasms Germ-Line Mutation BRCA1 Protein Mutation Female Carcinoma, Ovarian Epithelial Deep Learning
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DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.

Genome medicine Feb 25, 2022
BACKGROUND: Identifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employing systemic treatment strategies and risk-reducing interventions. However, current criteria and ris...
Deep Learning Genetic Predisposition to Disease Mutation Female Breast Neoplasms BRCA2 Protein Germ Cells DNA Repair Germ-Line Mutation BRCA1 Protein Humans Phenotype
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Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm.

American journal of human genetics Mar 23, 2021
The increasing scope of genetic testing allowed by next-generation sequencing (NGS) dramatically increased the number of genetic variants to be interpreted as pathogenic or benign for adequate patient management. Still, the interpretation process oft...
Humans Machine Learning Computer User Training Reproducibility of Results Genes, BRCA1 Datasets as Topic Germ-Line Mutation
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Genomic risk prediction of aromatase inhibitor-related arthralgia in patients with breast cancer using a novel machine-learning algorithm.

Cancer medicine Nov 23, 2017
Many breast cancer (BC) patients treated with aromatase inhibitors (AIs) develop aromatase inhibitor-related arthralgia (AIA). Candidate gene studies to identify AIA risk are limited in scope. We evaluated the potential of a novel analytic algorithm ...
Antineoplastic Agents, Hormonal Middle Aged Germ-Line Mutation Case-Control Studies Aromatase Inhibitors Genetic Predisposition to Disease Prognosis Humans Genomics Arthralgia Polymorphism, Single Nucleotide Female Machine Learning Breast Neoplasms Neoplasm Staging
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Discovering predisposing genes for hereditary breast cancer using deep learning.

Briefings in bioinformatics May 23, 2024
Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be iden...
Humans Female Pedigree Mutation, Missense Germ-Line Mutation Breast Neoplasms Deep Learning Genetic Predisposition to Disease
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Thousands of induced germline mutations affecting immune cells identified by automated meiotic mapping coupled with machine learning.

Proceedings of the National Academy of Sciences of the United States of America Jul 13, 2021
Forward genetic studies use meiotic mapping to adduce evidence that a particular mutation, normally induced by a germline mutagen, is causative of a particular phenotype. Particularly in small pedigrees, cosegregation of multiple mutations, occasiona...
Probability Automation Meiosis Germ-Line Mutation Mice, Inbred C57BL Flow Cytometry Leukocytes Male Mice Software Algorithms Phenotype Female Machine Learning Reproducibility of Results Animals
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Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma.

JAMA Nov 17, 2020
IMPORTANCE: Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection.
Genetic Testing High-Throughput Nucleotide Sequencing DNA Mutational Analysis Germ-Line Mutation Genetic Predisposition to Disease Predictive Value of Tests Humans Prostatic Neoplasms Middle Aged Sensitivity and Specificity Neural Networks, Computer Deep Learning Cross-Sectional Studies Female Male Melanoma
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