Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in l...

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Veröffentlicht in:	BMC bioinformatics Jg. 21; H. 1; S. 235 - 14
Hauptverfasser:	ElAbd, Hesham, Bromberg, Yana, Hoarfrost, Adrienne, Lenz, Tobias, Wendorff, Mareike
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	2230 Support BMC 09.06.2020 BioMed Central BioMed Central Ltd Springer Nature B.V
Schlagworte:	Algorithms Amino acid encoding Amino acids Amino Acids - metabolism Amino acids embedding Artificial neural networks Bioinformatics Biomedical and Life Sciences Cable television broadcasting industry Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Convoluted-neural network (CNN) Cybernetics, Artificial Intelligence and Robotics Data mining Deep learning Deep Learning - standards Embedding HLA-II peptide interaction Humans Iterative methods Learning algorithms Life Sciences Machine learning Machine Learning and Artificial Intelligence in Bioinformatics Mathematical analysis Matrix algebra Matrix methods Methodology Methodology Article Microarrays Neural networks Nucleotides Observational learning Peptides Protein-protein interaction (PPI) Protein-protein interactions Proteins Recurrent neural networks Technology application Training United States Recurrent Neural Network (rnn) Human-Leukocyte Antigen (hla) Amino Acids Embedding Amino Acid Encoding Hla-Ii Peptide Interaction Deep-Learning Convoluted-Neural Network (cnn) Machine-Learning (ml) Protein-Protein Interaction (ppi) Protein-protein interaction (PPI) HLA-II peptide interaction Deep-learning Convoluted-neural network (CNN) Recurrent neural network (RNN) Human-leukocyte antigen (HLA) Machine-learning (ML) Amino acid encoding Amino acids embedding
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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