Name: HLA-bind
Owner: Computational Biology and Computational Learning @ UCI
Description: Amino acid embedding and Convolutional Neural Network for HLA Class I-peptide binding prediction
Created: 2016-12-21 20:14:08.0
Updated: 2018-05-05 10:36:32.0
Pushed: 2017-08-03 21:35:14.0
Homepage: null
Size: 8213
Language: Python
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Author: ysvang@uci.edu
Usage: python main.py config.ini
Overview: HLA-CNN tool can be used to make binding prediction on HLA Class I peptides based on convolutional neural networks and a distributed representation of amino acids, HLA-Vec. At a high level, the tool consists of (a) an unsupervised, distributed vector representation learner for raw peptide sequence, (b) a training mode to learn weights to the classifier, (c) an evaluation mode to calculate Spearman's rank correlation coefficient (SRCC) and are under the receiver operating characteristic curve (AUC), (d) an inference mode to make prediction new peptides.
Pipeline: The pipeline is specified in the config.ini file. A config file is required to specify the parameters used in the various learning algorithm as well as files and directories.
Notes:
License: This project is licensed under the MIT License - see the LICENSE.md file for details.
Requirements:
Reference: Vang, Y. S. and Xie, X. (2017) HLA class I binding prediction via convolutional neural networks. https://doi.org/10.1093/bioinformatics/btx264