SUPPLEMENTARY MATERIAL: VisuNet: an interactive tool for rule network visualization of rule-based learning models

Karolina Smolinska Garbulowska - Uppsala University, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics / Science for Life Laboratory, SciLifeLab

Mateusz Garbulowski - Uppsala University, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics

Klev Diamanti - Uppsala University, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics, Department of Immunology, Genetics and Pathology / Science for Life Laboratory, SciLifeLab

Xavier Davoy - The Grenoble Institute of Technology– Phelma

Stephen Omondi Otieno Anyango - Uppsala University, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics

Fredrik Barrenäs - Uppsala University, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics

Susanne Bornelöv - Uppsala University / University of Cambridge, Department of Medical Biochemistry and Microbiology, Department of Cell and Molecular Biology / Cancer Research UK Cambridge Institute

Jan Komorowski - Uppsala University, Swedish Collegium for Advanced Study (SCAS), Department of Cell and Molecular Biology, Computational Biology and Bioinformatics

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The data consists of a pdf file with visualizations and an excel file with following tables:

- Table S1 The overview of applications.

- Table S2 SFARI result for 4 genes overlapped the rule-network of case-control study of autism and the SFARI genes databese.

- Table S3 GO over-representation results for the rule-network of case-control study of autism.

- Table S4 Full list of variables used for the rule-based machine learning schema. The list includesmetabolites and pools of metabolites.

- Table S5 List of rules for the R.ROSETTA model including support, accuracy and BH-corrected p-value for each rule.

The dataset was originally published in DiVA and moved to SND in 2024. Show less..