Code and data sets for "iDeepLC: A deep Learning-based retention time predictor for unseen modified peptides with a novel encoding system" to generate manuscript figures

Nameni, Alireza; Declercq, Arthur; Gabriels, Ralf; Devreese, Robbe; Martens, Lennart; Degroeve, Sven; Bouwmeester, Robbin

doi:10.5281/zenodo.15011301

Published March 12, 2025 | Version 1.00

Computational notebook Open

Code and data sets for "iDeepLC: A deep Learning-based retention time predictor for unseen modified peptides with a novel encoding system" to generate manuscript figures

1. VIB-UGent Center for Medical Biotechnology

Scripts used to train iDeepLC models, evaluate retention time predictions and generate manuscript figures. After unzipping the "Zenodo_iDeepLC.zip" file, see README.md for detailed information on how to generate figures and how the folder structure needs to be.

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Zenodo_iDeepLC.zip

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Zenodo_iDeepLC.zip md5:5f16d8739b05c49c955f37fadd63acbd	7.9 GB	Preview Download

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European Commission
PROTrEIN - Computational Proteomics Training European Innovative Network 956148

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Computational notebook

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Zenodo

License: Creative Commons Attribution 4.0 International

The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more

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Created: March 12, 2025
Modified: June 2, 2025

Code and data sets for "iDeepLC: A deep Learning-based retention time predictor for unseen modified peptides with a novel encoding system" to generate manuscript figures

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Files

Zenodo_iDeepLC.zip

Files (7.9 GB)

Additional details

Funding