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National Repository of Grey Literature 1 records found  Search took 0.00 seconds. 
Auto-Encoding Amino Acid Sequences with LSTM
 PROMBERGER, Markus
In this thesis a sequence to sequence autoencoder for amino acid sequences is constructed. The latent representation of the autoencoder is then used to classify the amino acid sequences according to their animal kingdom. The data consists of sequences from three different kingdoms, mammals, fish and birds. The thesis includes the preprocessing necessary for the data, the construction of the sequence to sequence autoencoder and the process of classification in the latent space.
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