Missing value estimation methods for DNA methylation data

Bioinformatics
Pietro Di LenaChristine Nardini

Abstract

DNA methylation is a stable epigenetic mark with major implications in both physiological (development, aging) and pathological conditions (cancers and numerous diseases). Recent research involving methylation focuses on the development of molecular age estimation methods based on DNA methylation levels (mAge). An increasing number of studies indicate that divergences between mAge and chronological age may be associated to age-related diseases. Current advances in high-throughput technologies have allowed the characterization of DNA methylation levels throughout the human genome. However, experimental methylation profiles often contain multiple missing values that can affect the analysis of the data and also mAge estimation. Although several imputation methods exist, a major deficiency lies in the inability to cope with large datasets, such as DNA methylation chips. Specific methods for imputing missing methylation data are therefore needed. We present a simple and computationally efficient imputation method, metyhLImp, based on linear regression. The rationale of the approach lies in the observation that methylation levels show a high degree of inter-sample correlation. We performed a comparative study of our approach with oth...Continue Reading

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Citations

Jun 12, 2020·Journal of Biomedical Informatics·Zahra MomeniRiccardo Bellazzi
Jan 9, 2021·Bioinformatics·Octavio Morante-Palacios, Esteban Ballestar
May 27, 2021·Nucleic Acids Research·Pietro Di LenaChristine Nardini
Jul 3, 2021·The Journal of Clinical Endocrinology and Metabolism·Hakyung KimSoo Heon Kwak
Nov 10, 2021·Future Oncology·Abdulazeez GiwaHocine Bendou
Nov 1, 2021·Bioinformatics·Gaetan De WaeleWillem Waegeman

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