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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
Published on: September 18, 2020
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Using epigenomics data to predict gene expression in lung cancer.
BMC Bioinformatics
|April 11, 2015
Summary
A new machine learning model accurately predicts gene expression changes in lung cancer using epigenomic and genomic data. This approach enhances understanding of cancer-related gene regulation.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Epigenetic alterations are linked to gene expression changes in diseases like cancer.
- Quantitative models for predicting gene expression are currently limited.
Purpose of the Study:
- To develop a machine learning model for predicting differential gene expression in lung cancer.
- To identify key epigenomic and genomic features influencing gene expression.
Main Methods:
- Utilized CpG methylation data (Illumina HumanMethylation450K) and histone modification data (ChIP-Seq).
- Integrated features from CpG methylation, histone modification, nucleotide composition, and conservation.
- Employed feature selection (ReliefF) and classification (Random Forest) with 10-fold cross-validation.
