Auto-Encoding Amino Acid Sequences with LSTM

Promberger, Markus

dc.contributor.advisor	Hochreiter, Sepp
dc.contributor.author	Promberger, Markus
dc.date.accessioned	2025-03-06T08:05:38Z
dc.date.available	2025-03-06T08:05:38Z
dc.date.issued	2022
dc.date.submitted	2022-03-08
dc.identifier.uri	https://dspace.jcu.cz/handle/20.500.14390/46152
dc.description.abstract	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.	cze
dc.format	57
dc.format	57
dc.language.iso	eng
dc.publisher	Jihočeská univerzita	cze
dc.rights	Bez omezení
dc.subject	machine learning	cze
dc.subject	sequence to sequence autoencoder	cze
dc.subject	amino acid sequence	cze
dc.subject	bioinformatic	cze
dc.subject	sequence alignment	cze
dc.subject	clustering	cze
dc.subject	machine learning	eng
dc.subject	sequence to sequence autoencoder	eng
dc.subject	amino acid sequence	eng
dc.subject	bioinformatic	eng
dc.subject	sequence alignment	eng
dc.subject	clustering	eng
dc.title	Auto-Encoding Amino Acid Sequences with LSTM	cze
dc.title.alternative	Auto-Encoding Amino Acid Sequences with LSTM	eng
dc.type	bakalářská práce	cze
dc.identifier.stag	61194
dc.description.abstract-translated	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.	eng
dc.date.accepted	2022-03-24
dc.description.department	Přírodovědecká fakulta	cze
dc.thesis.degree-discipline	Bioinformatics	cze
dc.thesis.degree-grantor	Jihočeská univerzita. Přírodovědecká fakulta	cze
dc.thesis.degree-name	Bc.
dc.thesis.degree-program	Applied Informatics	cze
dc.description.grade	Dokončená práce s úspěšnou obhajobou	cze
dc.contributor.referee	Paischer, Fabian
dc.description.defence	<p>Committe : Rudolf Vohnout, Ales Horak, Alois Regl, Marta Vohnoutova</p> <p>Student adheres time limit for the defense.</p> <p>From the reveiws discsussion begun - mainly concerning data sets used for the thesis, their qantity and origin. Also the question if this apoproach is feasible to replace traditional phylogenetic trees arises.</p>	cze