Analysis and classification of long terminal repeat (LTR) sequences using machine learning approaches – Bc. Jakub Horváth
Bc. Jakub Horváth
Master's thesis
Analysis and classification of long terminal repeat (LTR) sequences using machine learning approaches
Analysis and classification of long terminal repeat (LTR) sequences using machine learning approaches
Abstract:
Táto práca sa zameriava na analýzu a klasifikáciu sekvencií Long Terminal Repeats (LTR), ktoré sú kritickými zložkami retrotranspozónov, zohrávajúce významnú úlohu v štruktúre a evolúcii genómu. Práca využíva techniky vyhľadávania častých vzorov (Pattern mining) na identifikáciu významných spoluvýskytov transkripčných motívov v sekvenciách LTR s cieľom charakterizovať ich rozmanitosť a distribúciu …moreAbstract:
This thesis focuses on the analysis and classification of long terminal repeat (LTR) sequences, which are critical components of retrotransposons that play a significant role in genome structure and evolution. The work employs frequent pattern-mining techniques to identify significantly co-occurring motifs in LTR sequences, with the goal of characterizing their diversity and distribution. For the classification …more
Language used: English
Date on which the thesis was submitted / produced: 16. 5. 2023
Identifier:
https://is.muni.cz/th/m8eg3/
Thesis defence
- Date of defence: 21. 6. 2023
- Supervisor: Ing. Matej Lexa, Ph.D.
- Reader: doc. Mgr. Bc. Vít Nováček, PhD
Citation record
ISO 690-compliant citation record:
HORVÁTH, Jakub. \textit{Analysis and classification of long terminal repeat (LTR) sequences using machine learning approaches}. Online. Master's thesis. Brno: Masaryk University, Faculty of Informatics. 2023. Available from: https://theses.cz/id/8rskwi/.
Full text of thesis
Contents of on-line thesis archive
Published in Theses:- světu
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Institution archiving the thesis and making it accessible: Masarykova univerzita, Fakulta informatikyMasaryk University
Faculty of InformaticsMaster programme / field:
Artificial intelligence and data processing / Bioinformatics and systems biology
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