ArticleBMC bioinformatics2015
Using epigenomics data to predict gene expression in lung cancer.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Molecular signatures and machine learning driven stress biomarkers for rainbow trout aquaculture and climate adaptation.Scientific reports · 2025Article
- Temporally discordant chromatin accessibility and DNA demethylation define short- and long-term enhancer regulation during cell fate specification.Cell reports · 2025Article
- Article
- MethNet: a robust approach to identify regulatory hubs and their distal targets from cancer data.Nature communications · 2024Article
- Predicting gene expression state and prioritizing putative enhancers using 5hmC signal.Genome biology · 2024Article
- A miRNA-based epigenetic molecular clock for biological skin-age prediction.Archives of dermatological research · 2024Article
- A Deep Learning Approach for Prognostic Evaluation of Lung Adenocarcinoma Based on Cuproptosis-Related Genes.Biomedicines · 2023Article
- Epigenetic targets in B- and T-cell lymphomas: latest developments.Therapeutic advances in hematology · 2023Review
- Predicting gene expression from histone modifications with self-attention based neural networks and transfer learning.Frontiers in genetics · 2022Article
- Predicting liver cancer on epigenomics data using machine learning.Frontiers in bioinformatics · 2022Article
- Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification.Clinical epigenetics · 2020Review
- Specific enhancer selection by IRF3, IRF5 and IRF9 is determined by ISRE half-sites, 5' and 3' flanking bases, collaborating transcription factors and the chromatin environment in a combinatorial fashion.Nucleic acids research · 2020Article
- DNA methylation data-based molecular subtype classification and prediction in patients with gastric cancer.Cancer cell international · 2020Article
- The Role of Aberrant DNA Methylation in Misregulation of Gene Expression in Gonadotroph Nonfunctioning Pituitary Tumors.Cancers · 2019Article
- BioMethyl: an R package for biological interpretation of DNA methylation data.Bioinformatics (Oxford, England) · 2019Article
- On the problem of confounders in modeling gene expression.Bioinformatics (Oxford, England) · 2019Review
- RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes.BMC genomics · 2018Article
- Robust prediction of gene regulation in colorectal cancer tissues from DNA methylation profiles.Epigenetics · 2018Article
- Onco-Multi-OMICS Approach: A New Frontier in Cancer Research.BioMed research international · 2018Review
- Machine learning for epigenetics and future medical applications.Epigenetics · 2017Review
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Authors and funding
4 authors.
Funding
Abstract
backgroundEpigenetic alterations are known to correlate with changes in gene expression among various diseases including cancers. However, quantitative models that accurately predict the up or down regulation of gene expression are currently lacking.
methodsA new machine learning-based method of gene expression prediction is developed in the context of lung cancer. This method uses the Illumina Infinium HumanMethylation450K Beadchip CpG methylation array data from paired lung cancer and adjacent normal tissues in The Cancer Genome Atlas (TCGA) and histone modification marker CHIP-Seq data from the ENCODE project, to predict the differential expression of RNA-Seq data in TCGA lung cancers. It considers a comprehensive list of 1424 features spanning the four categories of CpG methylation, histone H3 methylation modification, nucleotide composition, and conservation. Various feature selection and classification methods are compared to select the best model over 10-fold cross-validation in the training data set.
resultsA best model comprising 67 features is chosen by ReliefF based feature selection and random forest classification method, with AUC = 0.864 from the 10-fold cross-validation of the training set and AUC = 0.836 from the testing set. The selected features cover all four data types, with histone H3 methylation modification (32 features) and CpG methylation (15 features) being most abundant. Among the dropping-off tests of individual data-type based features, removal of CpG methylation feature leads to the most reduction in model performance. In the best model, 19 selected features are from the promoter regions (TSS200 and TSS1500), highest among all locations relative to transcripts. Sequential dropping-off of CpG methylation features relative to different regions on the protein coding transcripts shows that promoter regions contribute most significantly to the accurate prediction of gene expression.
conclusionsBy considering a comprehensive list of epigenomic and genomic features, we have constructed an accurate model to predict transcriptomic differential expression, exemplified in lung cancer.
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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.