ReviewFrontiers in cell and developmental biology2021
KNNCNV: A K-Nearest Neighbor Based Method for Detection of Copy Number Variations Using NGS Data.
Review in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metagenomics next-generation sequencing for the diagnosis of central nervous system infection: A systematic review and meta-analysis.Frontiers in neurology · 2022Pooled it
- MTAP-Null Tumors: A Comprehensive Review on Synthetic Vulnerabilities and Therapeutic Strategies.Cells · 2025Review
- Research progress on predicting KRAS gene mutations in colorectal cancer by combining radiomics and multimodal medical imaging.Frontiers in oncology · 2025Review
- OTSUCNV: an adaptive segmentation and OTSU-based anomaly classification method for CNV detection using NGS data.BMC genomics · 2024Article
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
- Mitophagy genes in ovarian cancer: a comprehensive analysis for improved immunotherapy.Discover oncology · 2023Article
- Effective data filtering is prerequisite for robust microbial association network construction.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Copy number variation (CNV) is a well-known type of genomic mutation that is associated with the development of human cancer diseases. Detection of CNVs from the human genome is a crucial step for the pipeline of starting from mutation analysis to cancer disease diagnosis and treatment. Next-generation sequencing (NGS) data provides an unprecedented opportunity for CNVs detection at the base-level resolution, and currently, many methods have been developed for CNVs detection using NGS data. However, due to the intrinsic complexity of CNVs structures and NGS data itself, accurate detection of CNVs still faces many challenges. In this paper, we present an alternative method, called KNNCNV (K-Nearest Neighbor based CNV detection), for the detection of CNVs using NGS data. Compared to current methods, KNNCNV has several distinctive features: 1) it assigns an outlier score to each genome segment based solely on its first
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.