scholarly journals AthCNV: A Map of DNA Copy Number Variations in the Arabidopsis Genome

2020 ◽  
Vol 32 (6) ◽  
pp. 1797-1819 ◽  
Author(s):  
Agnieszka Zmienko ◽  
Malgorzata Marszalek-Zenczak ◽  
Pawel Wojciechowski ◽  
Anna Samelak-Czajka ◽  
Magdalena Luczak ◽  
...  
Keyword(s):  
Copy Number ◽  
Copy Number Variations ◽  
Arabidopsis Genome ◽  
Dna Copy Number ◽  
Dna Copy Number Variations

Abstract 751: Lung mitochondrial DNA copy number variations: E-cig users, smokers, and never-smokers

2021 ◽  
Author(s):  
Kellie M. Mori ◽  
Joseph P. McElroy ◽  
Daniel Y. Weng ◽  
Sangwoon Chung ◽  
Sarah A. Reisinger ◽  
...  
Keyword(s):  
Mitochondrial Dna ◽  
Copy Number ◽  
Copy Number Variations ◽  
Dna Copy Number ◽  
Dna Copy Number Variations ◽  
Mitochondrial Dna Copy Number ◽  
Never Smokers

Progress in finding pathogenic DNA copy number variations in dyslipidemia

2019 ◽  
Vol 30 (2) ◽  
pp. 63-70 ◽  
Author(s):  
Michael A. Iacocca ◽  
Jacqueline S. Dron ◽  
Robert A. Hegele
Keyword(s):  
Copy Number ◽  
Copy Number Variations ◽  
Dna Copy Number ◽  
Dna Copy Number Variations

P12: Detecting single DNA copy number variations in complex genomes using one nanogram of starting DNA and BAC-array CGH

2005 ◽  
Vol 48 (4) ◽  
pp. 459
Author(s):  
Marine Guillaud-Bataille ◽  
Alexander Valent ◽  
Pascal Soularue ◽  
Christine Perot ◽  
Maria del mar Inda ◽  
...  
Keyword(s):  
Copy Number ◽  
Array Cgh ◽  
Copy Number Variations ◽  
Dna Copy Number ◽  
Dna Copy Number Variations

scholarly journals A Genetic Risk Score for Glioblastoma Multiforme Based on Copy Number Variations

2021 ◽  
Author(s):  
Charmeine Ko ◽  
James P. Brody
Keyword(s):  
Machine Learning ◽  
Glioblastoma Multiforme ◽  
Copy Number ◽  
Germ Line ◽  
Genetic Test ◽  
Learning Algorithm ◽  
Copy Number Variations ◽  
Dna Copy Number ◽  
Dna Copy Number Variations ◽  
Tcga Dataset

AbstractGlioblastoma multiforme is the most common form of brain cancer. Several lines of evidence suggest that glioblastoma multiforme has a genetic basis. A genetic test that could identify people who are at high risk of developing glioblastoma multiforme could improve our understanding of this form of brain cancer.Using the Cancer Genome Atlas (TCGA) dataset, we found common germ line DNA copy number variations in the TCGA population. We tested whether different sets of these germ line DNA copy number variations could effectively distinguish patients with glioblastoma multiforme from others in the TCGA dataset. We used a gradient boosting machine, a machine learning classification algorithm, to classify TCGA patients solely based on a set of germline DNA copy number variations.We found that this machine learning algorithm could classify TCGA glioblastoma multiforme patients from the other TCGA patients with an area under the curve (AUC) of the receiver operating characteristic curve (AUC=0.875). Grouped into quintiles, the highest ranked quintile by the machine learning algorithm had an odds ratio of 3.78 (95% CI 3.25-4.40) higher than the average odds ratio and about 40 (95% CI 20-70) times higher than the lowest quintile.The identification of an effective germ line genetic test to stratify risk of developing glioblastoma multiforme should lead to a better understanding of how this cancer forms. This result might ultimately lead to better treatments of glioblastoma multiforme.

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