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undergraduate thesis
Generativni model za traženje terapeutskih peptidnih sekvenci
2024. urn:nbn:hr:190:582049
Antunović, Luka
University of Rijeka
Faculty of Engineering

Cite this document

Antunović, L. (2024). Generativni model za traženje terapeutskih peptidnih sekvenci (Undergraduate thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:582049

Antunović, Luka. "Generativni model za traženje terapeutskih peptidnih sekvenci." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2024. https://urn.nsk.hr/urn:nbn:hr:190:582049

Antunović, Luka. "Generativni model za traženje terapeutskih peptidnih sekvenci." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2024. https://urn.nsk.hr/urn:nbn:hr:190:582049

Antunović, L. (2024). 'Generativni model za traženje terapeutskih peptidnih sekvenci', Undergraduate thesis, University of Rijeka, Faculty of Engineering, accessed 18 December 2024, https://urn.nsk.hr/urn:nbn:hr:190:582049

Antunović L. Generativni model za traženje terapeutskih peptidnih sekvenci [Undergraduate thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2024 [cited 2024 December 18] Available at: https://urn.nsk.hr/urn:nbn:hr:190:582049

L. Antunović, "Generativni model za traženje terapeutskih peptidnih sekvenci", Undergraduate thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:190:582049

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Metadata
TitleGenerativni model za traženje terapeutskih peptidnih sekvenci
Title (english)Generative model for searching therapeutic peptide sequences
AuthorLuka Antunović
MentorGoran Mauša (mentor)
Committee memberMiroslav Joler (predsjednik povjerenstva)
Committee memberGoran Mauša (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
Rijeka
Defense date and country2024-09-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom radu analiziran je generativni model zasnovan na sprezi genetskog algoritma i strojnog učenja u svrhu pretraživanja terapeutskih peptidnih sekvenci. Detaljno je objašnjen rad genetskog algoritma, istaknute su prednosti i nedostaci odabira različitih ulaznih parametara te je analizirana konvergencija algoritma pretraživanja za generiranje peptidnih sekvenci kod ponovljenih mjerenja u nasumično odabranim početnim uzorcima. Analizom je utvrđeno da su terapeutske peptidne sekvence dobivene radom generativnog modela pretežno građene od aminokiselina arginin (R) i lizin (K) te konvergiraju ka duljini 15 aminokiselina. Generativni model svakim svojim izvršavanjem proizvodi različite populacije terapeutskih peptida, čineći ga idealnim za pretragu prostranog peptidnog prostora.
Abstract (english)
This paper presents a generative model based on the combination of a genetic algorithm and machine learning for the purpose of searching for therapeutic peptide sequences. The workings of the genetic algorithm are explained in detail, the advantages and disadvantages of selecting different input parameters are highlighted, and the convergence of the search algorithm for generating peptide sequences is analyzed across repeated measurements in randomly selected initial samples. The analysis determined that the therapeutic peptide sequences created by the generative model are predominantly composed of the amino acids arginine (R) and lysine (K) and tend to converge towards a length of 15 amino acids. The generative model produces different populations of therapeutic peptides with each execution, making it ideal for searching through the vast peptide space.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:582049
Study programmeTitle: Computer Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-09-12 07:19:16