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Tensorflow Based Deep Learning Model and Snakemake Workflow for Peptide-Protein Binding Predictions

By Gokmen Altay

Posted 06 Sep 2018
bioRxiv DOI: 10.1101/410928

In this study, we first present a Tensorflow based Deep Learning (DL) model that provides high performances in predicting the binding of peptides to major histocompatibility complex (MHC) class I protein. Second, we provide the necessary Python codes to run the model and also easily input large train and test peptide binding benchmark dataset. Third, we provide Snakemake based workflow that allows to run all the model and performance analysis over all the different test alleles at once in parallel over computer and clusters. We also provide comparison analysis of the performances of various models. Finally, in order to help attaining to the best possible DL model by a community effort, this work is intended to be a ready to modify base model and workflow for the global Deep Learning community with no domain knowledge in MHC-peptide binding problem and thus provides all the necessary reference code templates and benchmarking data sets for further developments on the presented model architecture. All the reproducible Python codes, Snakemake workflow and benchmark data sets and a tutorial are available online at https://github.com/altayg/Deep-Learning-MHCI.

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