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master's thesis
Analiza sličnosti peptida temeljena na latentnom prostoru autoenkodera
Rijeka: University of Rijeka, Faculty of Engineering, 2023. urn:nbn:hr:190:513051
Novosel, Bruno
University of Rijeka
Faculty of Engineering

Institutional repository: Repository of the University of Rijeka, Faculty of Engineering

Cite this document

Novosel, B. (2023). Analiza sličnosti peptida temeljena na latentnom prostoru autoenkodera (Master's thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:513051

Novosel, Bruno. "Analiza sličnosti peptida temeljena na latentnom prostoru autoenkodera." Master's thesis, University of Rijeka, Faculty of Engineering, 2023. https://urn.nsk.hr/urn:nbn:hr:190:513051

Novosel, Bruno. "Analiza sličnosti peptida temeljena na latentnom prostoru autoenkodera." Master's thesis, University of Rijeka, Faculty of Engineering, 2023. https://urn.nsk.hr/urn:nbn:hr:190:513051

Novosel, B. (2023). 'Analiza sličnosti peptida temeljena na latentnom prostoru autoenkodera', Master's thesis, University of Rijeka, Faculty of Engineering, accessed 23 June 2024, https://urn.nsk.hr/urn:nbn:hr:190:513051

Novosel B. Analiza sličnosti peptida temeljena na latentnom prostoru autoenkodera [Master's thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2023 [cited 2024 June 23] Available at: https://urn.nsk.hr/urn:nbn:hr:190:513051

B. Novosel, "Analiza sličnosti peptida temeljena na latentnom prostoru autoenkodera", Master's thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:190:513051

Metadata
TitleAnaliza sličnosti peptida temeljena na latentnom prostoru autoenkodera
Title (english)Peptide similarity analysis based on autoencoder latent space
AuthorBruno Novosel
MentorGoran Mauša (mentor)
Committee memberMladen Tomić (predsjednik povjerenstva)
Committee memberSandi Ljubić (član povjerenstva)
Committee memberGoran Mauša (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
Rijeka
Defense date and country2023, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Unatoč brzom napretku medicine i znanosti, mnoge bolesti i dalje predstavljaju veliku prijetnju ljudskom zdravlju. U tom kontekstu, peptidi su se istaknuli kao obećavajuće novo rješenje pri liječenju. Razne metode strojnog učenja pokazale su se vrlo učinkovitima za otkrivanje novih peptida sa željenim svojstvima. U ovom radu proveli smo istraživanje o primjeni varijacijskih autoenkodera za analizu sličnosti peptida s antimikrobnim i antiviralnim svojstvima kako bismo pokušali bolje shvatiti vezu između njihovih sekvenci i fizikalno-kemijskih značajki. Kao krajnji rezultat dobiven je VAE koji uspješno uči smislene prikaze peptida u dvodimenzionalnom latentnom prostoru, koji je uspješan u rekonstrukciji peptida iz latentnog prostora i koji se pokazao kao moćan pristup istraživanju strukturnih veza među peptidima. Potreban je dodatan trud i ulaganja u slična istraživanja jer imaju velik potencijal za ostvarenje napretka u medicini i poboljšanje kvalitete ljudskog života.
Abstract (english)
Despite the quick progress in medicine and science, there are still many diseases that pose a threat to human health. In this context, peptides stood out as a new promising treatment solution. Various machine learning methods have proven very effective in discovering new peptides with desired properties. In this paper, we conducted research on the application of variational autoencoders for similarity analysis of peptides with antimicrobial and antiviral properties so we could better understand the connection between their sequences and physicochemical properties. As the end result we got a variational autoencoder that successfully learns meaningful representations of peptides in a two-dimensional latent space, that is successful in reconstructing peptides from latent space and has proven to be a powerful approach to researching structural bonds between peptides. Additional effort and investment in similar research is needed because of its great potential for achieving progress in medicine and improving the quality of human life.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:513051
Study programmeTitle: Computer Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra 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 on2023-09-12 14:08:50