Amino acid encoding for deep learning applications

Journal article

Open access

Peer reviewed

Hesham ElAbd, Yana Bromberg, Adrienne Hoarfrost, Tobias Lenz, Andre Franke and Mareike Wendorff

BMC bioinformatics, Vol.21(1), pp.235-235

06/09/2020

PMID: 32517697

Abstract

Amino acid encoding

Amino acids embedding

Convoluted-neural network (CNN)

Deep-learning

HLA-II peptide interaction

Human-leukocyte antigen (HLA)

Machine-learning (ML)

Methodology

Protein-protein interaction (PPI)

Recurrent neural network (RNN)

url

https://doi.org/10.1186/s12859-020-03546-xView

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Title: Amino acid encoding for deep learning applications
Creators: Hesham ElAbd - Kiel, Germany
Yana Bromberg - New Brunswick, NJ USA
Adrienne Hoarfrost - New Brunswick, NJ USA
Tobias Lenz - 24306 Plön, Germany
Andre Franke - Kiel, Germany
Mareike Wendorff - Kiel, Germany
Publication Details: BMC bioinformatics, Vol.21(1), pp.235-235
Date published: 06/09/2020
Publisher: BioMed Central
Grant note: 80NSSC18M0093; NASA Astrobiology Postdoctoral Fellowship / ; Research Training Group 1743, ‘Genes, Environment and Inflammation; Research Training Group 1743, ‘Genes, Environment and Inflammation’ / ;
Academic Unit: Biochemistry and Microbiology (SEBS)
Language: English
Resource Type: Journal article
Identifiers: 991031665827404646