Original title: Auto-Encoding Amino Acid Sequences with LSTM
Authors: PROMBERGER, Markus
Document type: Bachelor's theses
Year: 2022
Language: eng
Abstract: [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.
Keywords: amino acid sequence; bioinformatic; clustering; machine learning; sequence alignment; sequence to sequence autoencoder
Citation: PROMBERGER, Markus. Auto-Encoding Amino Acid Sequences with LSTM. České Budějovice, 2022. bakalářská práce (Bc.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta

Institution: University of South Bohemia in České Budějovice (web)
Document availability information: Fulltext is available in the Digital Repository of University of South Bohemia.
Original record: http://www.jcu.cz/vskp/61194

Permalink: http://www.nusl.cz/ntk/nusl-516463


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Universities and colleges > Public universities > University of South Bohemia in České Budějovice
Academic theses (ETDs) > Bachelor's theses