ArticleBioinformatics (Oxford, England)2024
methyLImp2: faster missing value estimation for DNA methylation data.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- DNA methylation quality control decisions, removed beta values, and their influence on epigenetic clocks.Epigenomics · 2026Article
- Longitudinal Blood DNA Methylation Changes During Weight-Loss Intervention and Dementia Progression Risk.Research square · 2026Article
- slideimp: efficient imputation of DNA methylation data.Bioinformatics (Oxford, England) · 2026Article
- Development and validation of MethylCog, a blood DNA methylation proxy for cognition.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- DNA methylation signature of cognitive reserve moderates CSF tau pathology in prodromal Alzheimer's disease.Research square · 2026Article
- Multi-output learning for systematic missing value imputation in DNA methylation arrays.Bioinformatics advances · 2026Article
- DNA methylation signatures of Life's Essential 8 and their implications for dementia.Alzheimer's research & therapy · 2025Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- One-sample missing DNA-methylation value imputation.BMC bioinformatics · 2025Article
- Genome-wide methylome modeling via generative AI incorporating long- and short-range interactions.Science advances · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationmethyLImp, a method we recently introduced for the missing value estimation of DNA methylation data, has demonstrated competitive performance in data imputation compared to the existing, general-purpose, approaches. However, imputation running time was considerably long and unfeasible in case of large datasets with numerous missing values.
resultsmethyLImp2 made possible computations that were previously unfeasible. We achieved this by introducing two important modifications that have significantly reduced the original running time without sacrificing prediction performance. First, we implemented a chromosome-wise parallel version of methyLImp. This parallelization reduced the runtime by several 10-fold in our experiments. Then, to handle large datasets, we also introduced a mini-batch approach that uses only a subset of the samples for the imputation. Thus, it further reduces the running time from days to hours or even minutes in large datasets. AVAILABILITY AND IMPLEMENTATION: The R package methyLImp2 is under review for Bioconductor. It is currently freely available on Github https://github.com/annaplaksienko/methyLImp2.
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Registered trials
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.