AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples
Abstract The detection of somatic DNA variants in tumor samples with low tumor purity or sequencing depth remains a daunting challenge despite numerous attempts to address this problem. In this study, we constructed a substantially extended set of actual positive variants originating from a wide ran...
Saved in:
Main Authors: | Hyeonseong Jeon, Junhak Ahn, Byunggook Na, Soona Hong, Lee Sael, Sun Kim, Sungroh Yoon, Daehyun Baek |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2023-08-01
|
Series: | Experimental and Molecular Medicine |
Online Access: | https://doi.org/10.1038/s12276-023-01049-2 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Author Correction: AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples
by: Hyeonseong Jeon, et al.
Published: (2025-01-01) -
Tackling somatic DNA contamination in sperm epigenetic studies
by: Anamika Kumari, et al.
Published: (2025-02-01) -
Microsatellite instability and somatic gene variant profile in solid organ tumors
by: Ibrahim Halil Erdogdu, et al.
Published: (2024-05-01) -
Somatic Mutaome Profile in Human Cancer Tissues
by: Nayoung Kim, et al.
Published: (2013-12-01) -
Rare Somatic MEN1 Gene Pathogenic Variant in a Patient Affected by Atypical Parathyroid Adenoma
by: Luigia Cinque, et al.
Published: (2020-01-01)