ArticleJournal of cellular and molecular medicine2019
Predicting overall survival of patients with hepatocellular carcinoma using a three-category method based on DNA methylation and machine learning.
Article in Journal of cellular and molecular medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI.GigaScience · 2022Pooled it
- Comprehensive review and updated analysis of DNA methylation in hepatocellular carcinoma: From basic research to clinical application.Clinical and translational medicine · 2024Review
- Construction of molecular typing in LIHC microenvironment based on lipid metabolism-related genes.American journal of cancer research · 2023Article
- Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review.Translational gastroenterology and hepatology · 2022Review
- Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data.Laboratory investigation; a journal of technical methods and pathology · 2021Article
- A novel DNA methylation-based model that effectively predicts prognosis in hepatocellular carcinoma.Bioscience reports · 2021Article
- Triage 4.0: On Death Algorithms and Technological Selection. Is Today's Data- Driven Medical System Still Compatible with the Constitution?Journal of European CME · 2021Article
- Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review.World journal of gastroenterology · 2020Review
- A prognostic 10-lncRNA expression signature for predicting the risk of tumour recurrence in breast cancer patients.Journal of cellular and molecular medicine · 2019Article
- Predicting overall survival of patients with hepatocellular carcinoma using a three-category method based on DNA methylation and machine learning.Journal of cellular and molecular medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is closely associated with abnormal DNA methylation. In this study, we analyzed 450K methylation chip data from 377 HCC samples and 50 adjacent normal samples in the TCGA database. We screened 47,099 differentially methylated sites using Cox regression as well as SVM-RFE and FW-SVM algorithms, and constructed a model using three risk categories to predict the overall survival based on 134 methylation sites. The model showed a 10-fold cross-validation score of 0.95 and satisfactory predictive power, and correctly classified 26 of 33 samples in testing set obtained by stratified sampling from high, intermediate and low risk groups.
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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.