Demo Data from EDBT 2026 / reviewers in the wild / expert
Kristina Gervin
dblp:313/4746
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—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 · 87% Computational science and engineering · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › epigenomics
DNA methylation |
0.6 | 1 | 2022 | Modeling dependency structures in 450k DNA methylation data · Bioinform. 2022 |
Bioinformatics and computational biology
epigenomics |
0.6 | 1 | 2022 | Modeling dependency structures in 450k DNA methylation data · Bioinform. 2022 |
Computational science and engineering › spatial statistics
spatial dependency modeling |
0.2 | 1 | 2022 | Modeling dependency structures in 450k DNA methylation data · Bioinform. 2022 |
Methods — techniques the papers use, named apart from their topics
statistical modeling · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Modeling dependency structures in 450k DNA methylation dataabstractMOTIVATION: DNA methylation has been shown to be spatially dependent across chromosomes. Previous studies have focused on the influence of genomic context on the dependency structure, while not considering differences in dependency structure between individuals. RESULTS: We modeled spatial dependency with a flexible framework to quantify the dependency structure, focusing on inter-individual differences by exploring the association between dependency parameters and technical and biological variables. The model was applied to a subset of the Finnish Twin Cohort study (N = 1611 individuals). The estimates of the dependency parameters varied considerably across individuals, but were generally consistent across chromosomes within individuals. The variation in dependency parameters was associated with bisulfite conversion plate, zygosity, sex and age. The age differences presumably reflect accumulated environmental exposures and/or accumulated small methylation differences caused by stochastic mitotic events, establishing recognizable, individual patterns more strongly seen in older individuals. AVAILABILITY AND IMPLEMENTATION: The twin dataset used in the current study are located in the Biobank of the National Institute for Health and Welfare, Finland. All the biobanked data are publicly available for use by qualified researchers following a standardized application procedure (https://thl.fi/en/web/thl-biobank/for-researchers). A R-script for fitting the dependency structure to publicly available DNA methylation data with the software used in this article is provided in supplementary data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Haakon E. Nustad, Ingelin Steinsland, Miina Ollikainen, Emma Cazaly, Jaakko Kaprio, Yuval Benjamini, Kristina Gervin, Robert Lyle |
Bioinform. | 7 |