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Caroline May

dblp:180/9285 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
biomarker discovery
0.212016
PAA: an R/bioconductor package for biomarker discovery with protein microarrays · Bioinform. 2016
Bioinformatics and computational biology
feature selection
0.212016
PAA: an R/bioconductor package for biomarker discovery with protein microarrays · Bioinform. 2016
Bioinformatics and computational biology › proteomics
protein microarray data analysis
0.212016
PAA: an R/bioconductor package for biomarker discovery with protein microarrays · Bioinform. 2016
Bioinformatics and computational biology
proteomics
0.212016
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
YearPublicationVenuePosition
2016 PAA: an R/bioconductor package for biomarker discovery with protein microarrays
abstract
UNLABELLED: 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