Demo Data from EDBT 2026 / reviewers in the wild / expert
Matias Mendeville
dblp:247/1949
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-6130-1785ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
cancer genomics |
0.4 | 1 | 2019 | ACE: absolute copy number estimation from low-coverage whole-genome sequencing data · Bioinform. 2019 |
Bioinformatics and computational biology › cancer genomics › copy number analysis
copy number estimation |
0.4 | 1 | 2019 | ACE: absolute copy number estimation from low-coverage whole-genome sequencing data · Bioinform. 2019 |
Bioinformatics and computational biology › cancer genomics
tumor purity estimation |
0.1 | 1 | 2019 | ACE: absolute copy number estimation from low-coverage whole-genome sequencing data · Bioinform. 2019 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | ACE: absolute copy number estimation from low-coverage whole-genome sequencing dataabstractSUMMARY: Chromosomal copy number aberrations can be efficiently detected and quantified using low-coverage whole-genome sequencing, but analysis is hampered by the lack of knowledge on absolute DNA copy numbers and tumor purity. Here, we describe an analytical tool for Absolute Copy number Estimation, ACE, which scales relative copy number signals from chromosomal segments to optimally fit absolute copy numbers, without the need for additional genetic information, such as SNP data. In doing so, ACE derives an estimate of tumor purity as well. ACE facilitates analysis of large numbers of samples, while maintaining the flexibility to customize models and generate output of single samples. AVAILABILITY AND IMPLEMENTATION: ACE is freely available via www.bioconductor.org and at www.github.com/tgac-vumc/ACE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Jos B. Poell, Matias Mendeville, Daoud Sie, Arjen Brink, Ruud H. Brakenhoff, Bauke Ylstra |
Bioinform. | 2 |