Demo Data from VLDB 2026 / reviewers in the wild / expert
Matthew Zawistowski
dblp:233/4046
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › epigenomics › DNA methylation
DNA methylation analysis |
1.0 | 1 | 2026 | MethylModes: computationally efficient detection of multimodal distributions in DNA methylation data · Bioinform. 2026 |
Bioinformatics and computational biology › epigenomics
epigenome-wide association study |
0.3 | 1 | 2026 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | MethylModes: computationally efficient detection of multimodal distributions in DNA methylation dataabstractSUMMARY: 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 |