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DSI - Data Science Institute UHasselt


DSI - Data Science Institute UHasselt

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General data analysis

STAD - Simplified Topological Approximation of Data



This tool aids in the identification of global and local structures in complex datasets.

Keywords: topological data analysis, visual analytics

Type: R package, python library

More information at http://vda-lab.be/stad.html

Code: http://github.com/vda-lab/stad

Installation: pip install stad

Reference: https://arxiv.org/pdf/1907.05783.pdf

Contact: Jan Aerts



QCQuan analyzes your labeled LC-MS/MS proteomics differential expression experiment and provides you with (normalized) output files on both the non-redundant-peptide level as well as protein level, including a quality control and differential expression report in PDF format.

Keywords: proteomics workflow; mass spectrometry; differential expression; quality control; normalization

More information at https://qcquan.net/

Reference: https://doi.org/10.1021/acs.jproteome.9b00072

Contact: Joris Van Houtven



Methods that predict the monoisotopic mass based on the average mass are potentially affected by imprecisions associated with the average mass. To address this issue, we have developed a framework based on simple, linear models that allows prediction of the monoisotopic mass based on the exact mass of the most-abundant (aggregated) isotope peak, which is a robust measure of mass, insensitive to the aforementioned natural and technical causes

Type: R shiny app

More information at https://valkenborg-lab.shinyapps.io/mind/

Reference: https://www.ncbi.nlm.nih.gov/pubmed/31283196

Contact: Dirk Valkenborg 



BRAIN = Baffling Recursive Algorithm for Isotope distributioN calculations. This package calculates aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg.

Type: R Bioconductor

More information at: https://bioconductor.org/packages/release/bioc/html/BRAIN.html

Reference: https://www.ncbi.nlm.nih.gov/pubmed/23350948

Contact: Dirk Valkenborg


Clinical trial statistics


The R package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks.

Keywords: clinical trials; endpoints

More information at https://cran.r-project.org/web/packages/Surrogate/

Reference: Alonso, A., Bigirumurame, T., Burzykowski, T., et al. (2017). Applied Surrogate Endpoint Evaluation with SAS and R. Boca Raton: Chapman&Hall/CRC

Contact: Geert Molenberghs