Data and code underlying the publication: Overcoming Selection Bias in Synthetic Lethality Prediction

DOI:10.4121/07ba724a-330f-449f-8ce3-403a23f05d51.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: 10.4121/07ba724a-330f-449f-8ce3-403a23f05d51
Datacite citation style:
Seale, Colm; Tepeli, Yasin; Gonçalves, Joana (2025): Data and code underlying the publication: Overcoming Selection Bias in Synthetic Lethality Prediction. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/07ba724a-330f-449f-8ce3-403a23f05d51.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Software

This repository consists of code to reproduce the results of the paper "Overcoming Selection Bias in Synthetic Lethality Prediction".


The code to reproduce paper results is shared at: https://github.com/joanagoncalveslab/SBSL


No experimental data was collected as part of this project, this project only utilised previously publicly available datasets. Details on accessing this data is fully described in Section 2.1 of the published article.



History

  • 2025-02-18 first online, published, posted

Publisher

4TU.ResearchData

Format

R scripts and Rmarkdown files used to preprocess, train and evaluate SBSL and baseline models, and reproduce paper figures.

Funding

  • United States National Institutes of Health (grant code U54EY032442 to J.P.G.) United States National Institutes of Health
  • Holland Proton Theraphy Center (grant code 2019020 to C.S.) Holland Proton Theraphy Center

Organizations

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