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GiANT

Gene Set Uncertainty in Enrichment Analysis
An R package toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets.

Documentation

Documentation is included in the R package. You can get help for a command by typing ?<command> inside R when the package is loaded.

Installation

The most recent source and binary packages for BoolNet are available from CRAN.

Citing GiANT

To cite the package in publications, use:

Schmid F, Schmid M, Müssel C, Sträng JE, Buske C, Bullinger L, Kraus JM, Kestler HA. GiANT: gene set uncertainty in enrichment analysis. Bioinformatics 32(12): 1891-1894, 2016.  https://doi.org/10.1093/bioinformatics/btw030

Latest News

 

The position paper "Is there a role for statistics in artificial intelligence" has been published online first in Advances in Data Analysis and Classification.

 

Our paper "Corona Health - A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic" has been published in the International Journal of Environmental Research and Public Health.

 

Our paper "Patient empowerment during the COVID-19 pandemic: Ensuring safe and fast communication of test results" has been published in the Journal of Medical Internet Research.

 

Our paper "Perspective on mHealth Concepts to Ensure Users’ Empowerment–From Adverse Event Tracking for COVID-19 Vaccinations to Oncological Treatment" has been published in IEEE Access.

 

Our paper "Capturing dynamic relevance in Boolean networks using graph theoretical measures" has been published online first in Bioinformatics.

 

Our report protocol "Digitalization of adverse event management in oncology to improve treatment outcome—A prospective study protocol" has been published in PLoS One.

 

We are happy we could contribute to Beutel et al (2021) "A prospective Feasibility Trial to Challenge Patient-Derived Pancreatic Cancer Organoids in Predicting Treatment Response" published in MDPI Cancers.