Demo Data from EDBT 2026 / reviewers in the wild / expert
Giovanni Marchi
dblp:436/5294
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0001-9513-1123ORCID · reported
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 | FUSE: data-driven functional segmentation of DNA methylation data · Bioinform. 2026 |
Bioinformatics and computational biology
epigenomics |
1.0 | 1 | 2026 | FUSE: data-driven functional segmentation of DNA methylation data · Bioinform. 2026 |
Methods — techniques the papers use, named apart from their topics
data-driven segmentation · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | FUSE: data-driven functional segmentation of DNA methylation dataabstractSUMMARY: DNA methylation (DNAm) of neighbouring CpG sites is highly correlated, making DNAm function in terms of blocks. DNAm patterns and functionality are linked to both chromatin structure of DNA and gene regulation. Defining biologically meaningful DNA methylation blocks from whole-genome bisulfite sequencing (WGBS) data remains challenging, as most existing methods rely on fixed genomic windows rather than the observed methylation pattern. We present FUSE, a data-driven segmentation method that captures intrinsic methylation segments directly from WGBS data by jointly analyzing multiple samples. FUSE identifies spatially homogeneous methylation blocks shared across the input cohort while allowing different methylation states across samples. Applied to 61 WGBS samples from the ENCODE database, FUSE identified segments which overlap significantly with promoters, enhancers, and repetitive elements. FUSE was able to recover the true segment breakpoints in synthetic data with high sensitivity under increased levels of noise. As such, FUSE facilitates post hoc methylation analyses by aggregating coherent CpG sites into candidate segments for downstream differential methylation testing or other comparative studies. AVAILABILITY AND IMPLEMENTATION: FUSE is implemented as an R-package methFuse, available at https://github.com/holmsusa/methFuse and https://cran.r-project.org/package=methFuse. A GenomeSpy visualization of the data is available at https://csbi.ltdk.helsinki.fi/p/fuse_encode_gs/. Susanna Holmström, Antti Häkkinen, Kari Lavikka, Giovanni Marchi, Sampsa Hautaniemi, Alexandra Lahtinen |
Bioinform. | 4 |