ArticleBMC genomics2018
BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Epigenome-wide DNA methylation in externalizing behaviours: A review and combined analysis.Neuroscience and biobehavioral reviews · 2023Pooled it
- slideimp: efficient imputation of DNA methylation data.Bioinformatics (Oxford, England) · 2026Article
- Epigenetic biomarkers in inflammatory bowel diseases-computational challenges and opportunities.Journal of Crohn's & colitis · 2026Review
- mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data.Nature communications · 2026Article
- Deep Learning-Based DNA Methylation Detection in Cervical Cancer Using the One-Hot Character Representation Technique.Diagnostics (Basel, Switzerland) · 2025Article
- Direct genetic transformation bypasses tumor-associated DNA methylation alterations.Genome biology · 2025Article
- Improved epigenetic age prediction models by combining sex chromosome and autosomal markers.Epigenetics & chromatin · 2025Article
- Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing.Acta neuropathologica communications · 2025Article
- UNet with Attention Networks: A Novel Deep Learning Approach for DNA Methylation Prediction in HeLa Cells.Genes · 2025Article
- CMImpute: cross-species and tissue imputation of species-level DNA methylation samples across mammalian species.Genome biology · 2025Article
- Data-Driven Identification of Early Cancer-Associated Genes via Penalized Trans-Dimensional Hidden Markov Models.Biomolecules · 2025Article
- Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns.3 Biotech · 2024Article
- The role of epigenetics in women's reproductive health: the impact of environmental factors.Frontiers in endocrinology · 2024Review
- iCpG-Pos: an accurate computational approach for identification of CpG sites using positional features on single-cell whole genome sequence data.Bioinformatics (Oxford, England) · 2023Article
- Computational Methods for Single-cell DNA Methylome Analysis.Genomics, proteomics & bioinformatics · 2023Review
- CpG Transformer for imputation of single-cell methylomes.Bioinformatics (Oxford, England) · 2022Article
- Completing Single-Cell DNA Methylome ProfilesFrontiers in genetics · 2022Article
- A new classifier constructed with platelet features for malignant and benign pulmonary nodules based on prospective real-world data.Journal of Cancer · 2022Article
- Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh.PLOS global public health · 2022Article
- CUE: CpG impUtation ensemble for DNA methylation levels across the human methylation450 (HM450) and EPIC (HM850) BeadChip platforms.Epigenetics · 2021Article
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9 authors.
Funding
Abstract
backgroundBisulfite sequencing is widely employed to study the role of DNA methylation in disease; however, the data suffer from biases due to coverage depth variability. Imputation of methylation values at low-coverage sites may mitigate these biases while also identifying important genomic features associated with predictive power.
resultsHere we describe BoostMe, a method for imputing low-quality DNA methylation estimates within whole-genome bisulfite sequencing (WGBS) data. BoostMe uses a gradient boosting algorithm, XGBoost, and leverages information from multiple samples for prediction. We find that BoostMe outperforms existing algorithms in speed and accuracy when applied to WGBS of human tissues. Furthermore, we show that imputation improves concordance between WGBS and the MethylationEPIC array at low WGBS depth, suggesting improved WGBS accuracy after imputation.
conclusionsOur findings support the use of BoostMe as a preprocessing step for WGBS analysis.
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