ArticleScientific reports2019
Detection of chromosome structural variation by targeted next-generation sequencing and a deep learning application.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Diagnostic accuracy of 1p/19q codeletion tests in oligodendroglioma: A comprehensive meta-analysis based on a Cochrane systematic review.Neuropathology and applied neurobiology · 2022Pooled it
- Diagnostic test accuracy and cost-effectiveness of tests for codeletion of chromosomal arms 1p and 19q in people with glioma.The Cochrane database of systematic reviews · 2022Pooled it
- Pre-operative dual-time-point [European journal of nuclear medicine and molecular imaging · 2025Article
- Intrauterine phenotype, genetic analysis, and pregnancy follow-up of fetuses with the 16p12.2 microdeletion.Frontiers in genetics · 2025Article
- Artificial Intelligence and Chromothripsis.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach.Frontiers in computational neuroscience · 2024Article
- Global development of artificial intelligence in cancer field: a bibliometric analysis range from 1983 to 2022.Frontiers in oncology · 2023Article
- Towards a single-assay approach: a combined DNA/RNA sequencing panel eliminates diagnostic redundancy and detects clinically-relevant fusions in neuropathology.Acta neuropathologica communications · 2022Article
- Towards accurate and reliable resolution of structural variants for clinical diagnosis.Genome biology · 2022Review
- From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures.Computational and structural biotechnology journal · 2022Review
- Machine learning random forest for predicting oncosomatic variant NGS analysis.Scientific reports · 2021Article
- CIRCNV: Detection of CNVs Based on a Circular Profile of Read Depth from Sequencing Data.Biology · 2021Article
- Identification of prognosis-related molecular subgroups based on DNA methylation in pancreatic cancer.Clinical epigenetics · 2021Article
- Artificial Intelligence in Cancer Research and Precision Medicine.Cancer discovery · 2021Review
- Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics.Journal of clinical medicine · 2020Review
- Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma.Frontiers in oncology · 2020Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular testing is increasingly important in cancer diagnosis. Targeted next generation sequencing (NGS) is widely accepted method but structural variation (SV) detection by targeted NGS remains challenging. In the brain tumor, identification of molecular alterations, including 1p/19q co-deletion, is essential for accurate glial tumor classification. Hence, we used targeted NGS to detect 1p/19q co-deletion using a newly developed deep learning (DL) model in 61 tumors, including 19 oligodendroglial tumors. An ensemble 1-dimentional convolution neural network was developed and used to detect the 1p/19q co-deletion. External validation was performed using 427 low-grade glial tumors from The Cancer Genome Atlas (TCGA). Manual review of the copy number plot from the targeted NGS identified the 1p/19q co-deletion in all 19 oligodendroglial tumors. Our DL model also perfectly detected the 1p/19q co-deletion (area under the curve, AUC = 1) in the testing set, and yielded reproducible results (AUC = 0.9652) in the validation set (n = 427), although the validation data were generated on a completely different platform (SNP Array 6.0 platform). In conclusion, targeted NGS using a cancer gene panel is a promising approach for classifying glial tumors, and DL can be successfully integrated for the SV detection in NGS data.
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