Demo Data from VLDB 2026 / reviewers in the wild / expert
Caroline May
dblp:180/9285
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · unresolved
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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
biomarker discovery |
0.2 | 1 | 2016 | PAA: an R/bioconductor package for biomarker discovery with protein microarrays · Bioinform. 2016 |
Bioinformatics and computational biology
feature selection |
0.2 | 1 | 2016 | PAA: an R/bioconductor package for biomarker discovery with protein microarrays · Bioinform. 2016 |
Bioinformatics and computational biology › proteomics
protein microarray data analysis |
0.2 | 1 | 2016 | PAA: an R/bioconductor package for biomarker discovery with protein microarrays · Bioinform. 2016 |
Bioinformatics and computational biology
proteomics |
0.2 | 1 | 2016 | PAA: an R/bioconductor package for biomarker discovery with protein microarrays · Bioinform. 2016 |
Methods — techniques the papers use, named apart from their topics
support vector machine · 0.2recursive feature elimination · 0.2ensemble feature selection · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | PAA: an R/bioconductor package for biomarker discovery with protein microarraysabstractUNLABELLED: The R/Bioconductor package Protein Array Analyzer (PAA) facilitates a flexible analysis of protein microarrays for biomarker discovery (esp., ProtoArrays). It provides a complete data analysis workflow including preprocessing and quality control, uni- and multivariate feature selection as well as several different plots and results tables to outline and evaluate the analysis results. As a main feature, PAA's multivariate feature selection methods are based on recursive feature elimination (e.g. SVM-recursive feature elimination, SVM-RFE) with stability ensuring strategies such as ensemble feature selection. This enables PAA to detect stable and reliable biomarker candidate panels. AVAILABILITY AND IMPLEMENTATION: PAA is freely available (BSD 3-clause license) from http://www.bioconductor.org/packages/PAA/ CONTACT: [email protected] or [email protected]. Michael Turewicz, Maike Ahrens, Caroline May, Katrin Marcus, Martin Eisenacher |
Bioinform. | 3 |