Unknown

Dataset Information

0

Predicting overall survival of patients with hepatocellular carcinoma using a three-category method based on DNA methylation and machine learning.

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.

SUBMITTER: Dong RZ 

PROVIDER: S-EPMC6484308 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

Json Xml
altmetric image

Publications

Predicting overall survival of patients with hepatocellular carcinoma using a three-category method based on DNA methylation and machine learning.

Dong Rui-Zhao RZ   Yang Xuan X   Zhang Xin-Yu XY   Gao Ping-Ting PT   Ke Ai-Wu AW   Sun Hui-Chuan HC   Zhou Jian J   Fan Jia J   Cai Jia-Bin JB   Shi Guo-Ming GM  

Journal of cellular and molecular medicine 20190219 5

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  ...[more]

Similar Datasets

Unknown Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis.
| S-EPMC6173915 | biostudies-literature
Unknown Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery.
| S-EPMC7890310 | biostudies-literature
Unknown A genomic-clinicopathologic nomogram for predicting overall survival of hepatocellular carcinoma.
| S-EPMC7709450 | biostudies-literature
Unknown Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method.
| S-EPMC7122481 | biostudies-literature
Unknown Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning.
| S-EPMC7417437 | biostudies-literature
Transcriptomics Chromatin Interaction Neural Network (ChINN): A machine learning-based method for predicting chromatin interactions from DNA sequences
2021-07-09 | GSE163896 | GEO
Unknown Identification of a six-gene signature predicting overall survival for hepatocellular carcinoma.
| S-EPMC6528264 | biostudies-literature
Unknown Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category.
| S-EPMC5954128 | biostudies-literature
Unknown Identification of an autophagy-related gene signature predicting overall survival for hepatocellular carcinoma.
| S-EPMC7812060 | biostudies-literature
Unknown Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma.
| S-EPMC4610387 | biostudies-literature