Amino acid encoding for deep learning applications
Abstract
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 language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently,...
Paper Details
Title
Amino acid encoding for deep learning applications
Published Date
Jun 9, 2020
Journal
Volume
21
Issue
1
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