ArticleGenomics insights2014
Application of Machine Learning to Development of Copy Number Variation-based Prediction of Cancer Risk.
Article in Genomics insights, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 27 citations in OpenAlex.
- Triphasic Development of the Genetic Code.Chemical reviews · 2024Review
- Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision.Current issues in molecular biology · 2024Article
- Machine learning approaches to predict drug efficacy and toxicity in oncology.Cell reports methods · 2023Review
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
- Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning.Computational intelligence and neuroscience · 2022Article
- Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability.Human genomics · 2021Article
- Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.Human genomics · 2020Article
- Genomic subtyping of liver cancers with prognostic application.BMC cancer · 2020Article
- Pan-Cancer Classification Based on Self-Normalizing Neural Networks and Feature Selection.Frontiers in bioengineering and biotechnology · 2020Article
- Highly Recurrent Copy Number Variations inFrontiers in psychiatry · 2020Article
- AluScanCNV2: An R package for copy number variation calling and cancer risk prediction with next-generation sequencing data.Genes & diseases · 2019Article
- Article
- Feature co-localization landscape of the human genome.Scientific reports · 2016Article
- Copy number variation analysis based on AluScan sequences.Journal of clinical bioinformatics · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the present study, recurrent copy number variations (CNVs) from non-tumor blood cell DNAs of Caucasian non-cancer subjects and glioma, myeloma, and colorectal cancer-patients, and Korean non-cancer subjects and hepatocellular carcinoma, gastric cancer, and colorectal cancer patients, were found to reveal for each of the two ethnic cohorts highly significant differences between cancer patients and controls with respect to the number of CN-losses and size-distribution of CN-gains, suggesting the existence of recurrent constitutional CNV-features useful for prediction of predisposition to cancer. Upon identification by machine learning, such CNV-features could extensively discriminate between cancer-patient and control DNAs. When the CNV-features selected from a learning-group of Caucasian or Korean mixed DNAs consisting of both cancer-patient and control DNAs were employed to make predictions on the cancer predisposition of an unseen test group of mixed DNAs, the average prediction accuracy was 93.6% for the Caucasian cohort and 86.5% for the Korean cohort.
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Registered trials
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.