Název:
Auto-Encoding Amino Acid Sequences with LSTM
Autoři:
PROMBERGER, Markus Typ dokumentu: Bakalářské práce
Rok:
2022
Jazyk:
eng
Abstrakt: [eng][cze] 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.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.
Klíčová slova:
amino acid sequence; bioinformatic; clustering; machine learning; sequence alignment; sequence to sequence autoencoder Citace: PROMBERGER, Markus. Auto-Encoding Amino Acid Sequences with LSTM. České Budějovice, 2022. bakalářská práce (Bc.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
Instituce: Jihočeská univerzita v Českých Budějovicích
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Informace o dostupnosti dokumentu:
Plný text je dostupný v digitálním repozitáři JČU. Původní záznam: http://www.jcu.cz/vskp/61194