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Stephanie M. van de Ven

dblp:165/0711 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
0000-0003-0861-0471ORCID · 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 · 33% Medical and health informatics · 33% Computational science and engineering · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational science and engineering › pattern recognition
image classification
0.212015
Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments · Bioinform. 2015
Bioinformatics and computational biology › metabolomics
mass spectrometry imaging
0.212015
Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments · Bioinform. 2015
Medical and health informatics › medical imaging › medical image analysis
medical image segmentation
0.212015
Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments · Bioinform. 2015

Methods — techniques the papers use, named apart from their topics

regularization · 0.2mixture modeling · 0.2
YearPublicationVenuePosition
2015 Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments
abstract
Abstract Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization. Contact: [email protected] Availability and implementation: The code, the documentation, and examples are available open-source at www.cardinalmsi.org under the Artistic-2.0 license. The package is available at www.bioconductor.org.
Kyle D. Bemis, April Harry, Livia Eberlin, Christina Ferreira, Stephanie M. van de Ven, Parag Mallick, Mark L. Stolowitz, Olga Vitek
Bioinform.5