AIMC Topic: DNA Methylation

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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
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 meth...

Molecular and epigenetic profiles of BRCA1-like hormone-receptor-positive breast tumors identified with development and application of a copy-number-based classifier.

Breast cancer research : BCR
BACKGROUND: BRCA1-mutated cancers exhibit deficient homologous recombination (HR) DNA repair, resulting in extensive copy number alterations and genome instability. HR deficiency can also arise in tumors without a BRCA1 mutation. Compared with other ...

Manganese oxide nanoparticles induce genotoxicity and DNA hypomethylation in the moss Physcomitrella patens.

Mutation research. Genetic toxicology and environmental mutagenesis
The genotoxicity of nanoparticles is a major concern for nano-safety appraisal in the bryophytes as they are the primary colonizers of bare land, indicators of atmospheric pollution and excellent accumulators of trace metals. The present study for th...

LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer.

PloS one
Although modern methods of whole genome DNA methylation analysis have a wide range of applications, they are not suitable for clinical diagnostics due to their high cost and complexity and due to the large amount of sample DNA required for the analys...

Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20.

BMC genetics
BACKGROUND: Multiple layers of genetic and epigenetic variability are being simultaneously explored in an increasing number of health studies. We summarize here different approaches applied in the Data Mining and Machine Learning group at the GAW20 t...

Using recursive feature elimination in random forest to account for correlated variables in high dimensional data.

BMC genetics
BACKGROUND: Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact it...

Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy.

BMC bioinformatics
BACKGROUND: Spastic cerebral palsy (CP) is a leading cause of physical disability. Most people with spastic CP are born with it, but early diagnosis is challenging, and no current biomarker platform readily identifies affected individuals. The aim of...

The potential of circulating tumor DNA methylation analysis for the early detection and management of ovarian cancer.

Genome medicine
BACKGROUND: Despite a myriad of attempts in the last three decades to diagnose ovarian cancer (OC) earlier, this clinical aim still remains a significant challenge. Aberrant methylation patterns of linked CpGs analyzed in DNA fragments shed by cancer...

Commercial glucometer as signal transducer for simple evaluation of DNA methyltransferase activity and inhibitors screening.

Analytica chimica acta
DNA methyltransferase (MTase) plays an important role in many biological processes and has been recognized as a predictive cancer biomarker far before other signs of malignancy and a therapeutic target in cancer treatment. Thus simple and sensitive d...