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Annika Fust

dblp:237/9161 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › omics data analysis
omics data visualization
0.412019
WIlsON: Web-based Interactive Omics VisualizatioN · Bioinform. 2019
Bioinformatics and computational biology › drug discovery
high-throughput screening
0.112019
WIlsON: Web-based Interactive Omics VisualizatioN · Bioinform. 2019

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

r shiny · 0.4plotly · 0.4interactive dashboard · 0.4
YearPublicationVenuePosition
2019 WIlsON: Web-based Interactive Omics VisualizatioN
abstract
MOTIVATION: High throughput (HT) screens in the omics field are typically analyzed by automated pipelines that generate static visualizations and comprehensive spreadsheet data for scientists. However, exploratory and hypothesis driven data analysis are key aspects of the understanding of biological systems, both generating extensive need for customized and dynamic visualization. RESULTS: Here we describe WIlsON, an interactive workbench for analysis and visualization of multi-omics data. It is primarily intended to empower screening platforms to offer access to pre-calculated HT screen results to the non-computational scientist. Facilitated by an open file format, WIlsON supports all types of omics screens, serves results via a web-based dashboard, and enables end users to perform analyses and generate publication-ready plots. AVAILABILITY AND IMPLEMENTATION: We implemented WIlsON in R with a focus on extensibility using the modular Shiny and Plotly frameworks. A demo of the interactive workbench without limitations may be accessed at http://loosolab.mpi-bn.mpg.de. A standalone Docker container as well as the source code of WIlsON are freely available from our Docker hub https://hub.docker. com/r/loosolab/wilson, CRAN https://cran.r-project.org/web/packages/wilson/, and GitHub repository https://github.molgen.mpg.de/loosolab/wilson-apps, respectively.
Hendrik Schultheis, Carsten Kuenne, Jens Preussner, Rene Wiegandt, Annika Fust, Mette Bentsen, Mario Looso
Bioinform.5