Data underlying the publication: "GONNECT: Coupling Biological Systems to Neural Networks for Improved Model Interpretability"

DOI:10.4121/0d78788b-6bd7-4941-a942-245309107b6d.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/0d78788b-6bd7-4941-a942-245309107b6d

Datacite citation style

Lieftinck, Martijn; Verlaan, Timo; Reinders, Marcel (2025): Data underlying the publication: "GONNECT: Coupling Biological Systems to Neural Networks for Improved Model Interpretability". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/0d78788b-6bd7-4941-a942-245309107b6d.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This dataset contains all processed data required to reproduce the results of the GONNECT paper. In this work, we couple the structure of a neural network model to biological prior information, to gain interpretable activations in the neural network's hidden layers (see preprint/publication for more information). The data presented here includes processed gene expression data from The Cancer Genome Atlas (TCGA) data that is used as input data for the model, and both the raw and processed Gene Ontology (GO, https://geneontology.org/) knowledge base, from which the structure of the neural networks in this study is derived. There are also some miscellaneous files used to link genes to proteins. All data used in this study is publically available.


Code corresponding to the paper can be found here: https://github.com/DelftBioinformaticsLab/GONNECT

History

  • 2025-11-07 first online, published, posted

Publisher

4TU.ResearchData

Format

csv, csv.gz, txt, tsv, pkl, gaf

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Pattern Recognition and Bioinformatics

DATA

Files (13)