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Matthew Zawistowski

dblp:233/4046 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › epigenomics › DNA methylation
DNA methylation analysis
1.012026
MethylModes: computationally efficient detection of multimodal distributions in DNA methylation data · Bioinform. 2026
Bioinformatics and computational biology › epigenomics
epigenome-wide association study
0.312026
MethylModes: computationally efficient detection of multimodal distributions in DNA methylation data · Bioinform. 2026

Methods — techniques the papers use, named apart from their topics

kernel smoothing · 1.0
YearPublicationVenuePosition
2026 MethylModes: computationally efficient detection of multimodal distributions in DNA methylation data
abstract
SUMMARY: MethylModes is an R package and Shiny application to identify multimodal distributions in human DNA methylation at individual CpG sites. Multimodal distributions, which can be the result of nearby genetic variation, environmental exposures, or assay artifacts, are susceptible to confounding and important to identify for methylation analysis. MethylModes is easily incorporated into existing quality control pipelines of array-based DNA methylation data. The underlying algorithm uses kernel smoothing of probe-level data to locate the number and location of peaks. The algorithm can be parallelized across probes for efficient implementation at genome-scale. We provide a case study implementation of MethylModes in the Health and Retirement Study as well as the Airwave Health Monitoring Study. AVAILABILITY AND IMPLEMENTATION: MethylModes is available on GitHub at https://github.com/lutiffan/methylModes as an R package wrapping an R Shiny application. We include a toy dataset to validate installation. The codebase is also published on Zenodo at https://doi.org/10.5281/zenodo.17448517.
T. Sophia Luo, Jonathon LeFaive, John Dou, Kelly M. Bakulski, Erin B. Ware, Matthew Zawistowski
Bioinform.6