Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions

Ceder Dens; Wout Bittremieux; Fabio Affaticati; Kris Laukens; Pieter Meysman

doi:10.1101/2022.05.02.490264

New Results

Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions

View ORCID ProfileCeder Dens, View ORCID ProfileWout Bittremieux, View ORCID ProfileFabio Affaticati, View ORCID ProfileKris Laukens, View ORCID ProfilePieter Meysman

doi: https://doi.org/10.1101/2022.05.02.490264

Ceder Dens

1Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site
ORCID record for Ceder Dens

Wout Bittremieux

2Dorrestein Laboratory, University of California San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences, 9500 Gilman Drive, La Jolla, CA 92093, USA

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site
ORCID record for Wout Bittremieux

Fabio Affaticati

1Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site
ORCID record for Fabio Affaticati

Kris Laukens

1Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site
ORCID record for Kris Laukens

Pieter Meysman

1Adrem Data Lab, Department of Computer Science, University of Antwerp, Middelheimlaan 1, 2020 Antwerpen, Belgium

Find this author on Google Scholar
Find this author on PubMed
Search for this author on this site
ORCID record for Pieter Meysman
For correspondence: pieter.meysman@uantwerpen.be

Abstract
Full Text
Info/History
Metrics
Supplementary material
Data/Code
Preview PDF

https://doi.org/10.5281/zenodo.6500495

View the discussion thread.

PreviousNext

Posted October 04, 2022.

Download PDF

Supplementary Material

Data/Code

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Your Email *

Your Name *

Send To *

Enter multiple addresses on separate lines or separate them with commas.

You are going to email the following Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions

Message Subject (Your Name) has forwarded a page to you from bioRxiv

Message Body (Your Name) thought you would like to see this page from the bioRxiv website.

Your Personal Message

CAPTCHA

This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.

Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions

Ceder Dens, Wout Bittremieux, Fabio Affaticati, Kris Laukens, Pieter Meysman

bioRxiv 2022.05.02.490264; doi: https://doi.org/10.1101/2022.05.02.490264

Share This Article:

Citation Tools

Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interactions

Ceder Dens, Wout Bittremieux, Fabio Affaticati, Kris Laukens, Pieter Meysman

bioRxiv 2022.05.02.490264; doi: https://doi.org/10.1101/2022.05.02.490264

Citation Manager Formats

BibTeX
Bookends
EasyBib
EndNote (tagged)
EndNote 8 (xml)
Medlars
Mendeley
Papers
RefWorks Tagged
Ref Manager
RIS
Zotero

Tweet Widget
Facebook Like
Google Plus One

Subject Area

Bioinformatics

Subject Areas

All Articles

Animal Behavior and Cognition (4377)
Biochemistry (9568)
Bioengineering (7080)
Bioinformatics (24814)
Biophysics (12594)
Cancer Biology (9940)
Cell Biology (14318)
Clinical Trials (138)
Developmental Biology (7940)
Ecology (12090)
Epidemiology (2067)
Evolutionary Biology (15971)
Genetics (10911)
Genomics (14721)
Immunology (9856)
Microbiology (23611)
Molecular Biology (9468)
Neuroscience (50791)
Paleontology (369)
Pathology (1537)
Pharmacology and Toxicology (2677)
Physiology (4004)
Plant Biology (8651)
Scientific Communication and Education (1507)
Synthetic Biology (2388)
Systems Biology (6419)
Zoology (1345)