EDBT 2026 Demo / reviewers in the wild / expert
Christophe M. Olinger
dblp:370/5505
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
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 |
Medical and health informatics · 77% Bioinformatics and computational biology · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics › clinical diagnosis
molecular diagnostics |
0.7 | 1 | 2023 | Digital PCR cluster predictor: a universal R-package and shiny app for the automated analysis of multiplex digital PCR data · Bioinform. 2023 |
Bioinformatics and computational biology
nucleic acid quantification |
0.2 | 1 | 2023 | Digital PCR cluster predictor: a universal R-package and shiny app for the automated analysis of multiplex digital PCR data · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
clustering · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Digital PCR cluster predictor: a universal R-package and shiny app for the automated analysis of multiplex digital PCR dataabstractDigital polymerase chain reaction (dPCR) is an emerging technology that enables accurate and sensitive quantification of nucleic acids. Most available dPCR systems have two channel optics, with ad hoc software limited to the analysis of single and duplex assays. Although multiplexing strategies were developed, variable assay designs, dPCR systems, and the analysis of low DNA input data restricted the ability for a universal automated clustering approach. To overcome these issues, we developed dPCR Cluster Predictor (dPCP), an R package and a Shiny app for automated analysis of up to 4-plex dPCR data. dPCP can analyse and visualize data generated by multiple dPCR systems carrying out accurate and fast clustering not influenced by the amount and integrity of input of nucleic acids. With the companion Shiny app, the functionalities of dPCP can be accessed through a web browser. Alfonso De Falco, Christophe M. Olinger, Barbara Klink, Michel Mittelbronn, Daniel Stieber |
Bioinform. | 2 |