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undergraduate thesis
Izrada modela predviđanja aktivnosti peptida
2020. urn:nbn:hr:190:213926
Novosel, Bruno
University of Rijeka
Faculty of Engineering
Department of Computer Engineering
Section of Intelligent Computing Systems

Cite this document

Novosel, B. (2020). Izrada modela predviđanja aktivnosti peptida (Undergraduate thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:213926

Novosel, Bruno. "Izrada modela predviđanja aktivnosti peptida." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2020. https://urn.nsk.hr/urn:nbn:hr:190:213926

Novosel, Bruno. "Izrada modela predviđanja aktivnosti peptida." Undergraduate thesis, University of Rijeka, Faculty of Engineering, 2020. https://urn.nsk.hr/urn:nbn:hr:190:213926

Novosel, B. (2020). 'Izrada modela predviđanja aktivnosti peptida', Undergraduate thesis, University of Rijeka, Faculty of Engineering, accessed 23 December 2024, https://urn.nsk.hr/urn:nbn:hr:190:213926

Novosel B. Izrada modela predviđanja aktivnosti peptida [Undergraduate thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2020 [cited 2024 December 23] Available at: https://urn.nsk.hr/urn:nbn:hr:190:213926

B. Novosel, "Izrada modela predviđanja aktivnosti peptida", Undergraduate thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:190:213926

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Metadata
TitleIzrada modela predviđanja aktivnosti peptida
Title (english)Creating a peptide activity prediction model
AuthorBruno Novosel
MentorGoran Mauša (mentor)
Committee memberKristijan Lenac (predsjednik povjerenstva)
Committee memberGoran Mauša (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Computer Engineering)
(Section of Intelligent Computing Systems)
Rijeka
Defense date and country2020-07-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Virusi i dalje predstavljaju glavni uzrok bolesti kod ljudi usprkos masovnoj proizvodnji cjepiva i antivirusnih lijekova, a velik potencijal za nove pomake u tom području predstavljaju peptidi s antiviralnom aktivnošću. Kako bi efikasno otkrivali nove takve peptide potrebno je koristiti neku vrstu predviđanja, a pokazano je da je to moguće na osnovi njihovih fizikalno-kemijskih svojstava. Takvo predviđanje, ako se dobro izvede, može biti vrlo učinkovito. Posebno se istaknulo strojno učenje metodom random forest, kakvo je implementirano i u ovom radu. Daljnjim trudom i ulaganjem u ovakva istraživanja može se ostvariti velik napredak u medicini i poboljšati kvaliteta ljudskog života.
Abstract (english)
Viruses are still the main cause of human diseases despite the mass production of vaccines and antiviral drugs. Antiviral peptides have a great potential to change things for the better in that particular field. For efficient discovery of such new peptides, some kind of prediction should be used and it was shown that this was possible based on their physicochemical properties. Such prediction, if done right, can be very effective. Machine learning using the random forest method was proven especially efficient, which was also implemented in this study. With further effort and investments in such research, a great progress in medicine can be achieved and the quality of human lives improved.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:213926
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 conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2020-12-16 16:27:06