prikaz prve stranice dokumenta Metode predviđanja interakcija lijekova i ciljanih molekula
  Download
PDF 990.97 KB
master's thesis
Metode predviđanja interakcija lijekova i ciljanih molekula
Zagreb: University of Zagreb, Faculty of Science, 2021. urn:nbn:hr:217:074538
Bratulić, Petar
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

Cite this document

Bratulić, P. (2021). Metode predviđanja interakcija lijekova i ciljanih molekula (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:074538

Bratulić, Petar. "Metode predviđanja interakcija lijekova i ciljanih molekula." Master's thesis, University of Zagreb, Faculty of Science, 2021. https://urn.nsk.hr/urn:nbn:hr:217:074538

Bratulić, Petar. "Metode predviđanja interakcija lijekova i ciljanih molekula." Master's thesis, University of Zagreb, Faculty of Science, 2021. https://urn.nsk.hr/urn:nbn:hr:217:074538

Bratulić, P. (2021). 'Metode predviđanja interakcija lijekova i ciljanih molekula', Master's thesis, University of Zagreb, Faculty of Science, accessed 26 November 2024, https://urn.nsk.hr/urn:nbn:hr:217:074538

Bratulić P. Metode predviđanja interakcija lijekova i ciljanih molekula [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2021 [cited 2024 November 26] Available at: https://urn.nsk.hr/urn:nbn:hr:217:074538

P. Bratulić, "Metode predviđanja interakcija lijekova i ciljanih molekula", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:217:074538

Metadata
TitleMetode predviđanja interakcija lijekova i ciljanih molekula
Title (english)Methods for predicting drug-target molecule interactions
AuthorPetar Bratulić
MentorTomislav Šmuc (mentor)
Committee memberTomislav Šmuc (predsjednik povjerenstva)
Committee memberMarko Horvat (član povjerenstva)
Committee memberNenad Antonić (član povjerenstva)
Committee memberZoran Vondraček (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2021-03-04, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
U ovom radu analiziran je problem predviđanja interakcija lijekova i ciljanih molekula (DTI predviđanje). Predstavljeni su scenariji modeliranja interakcija lijekova i ciljanih molekula, kao i moderni algoritmi strojnog učenja koji se koriste u DTI predviđanju. Cilj rada bio je ocijeniti različite pristupe u kontekstu tih, različitih scenarija predviđanja interakcija. Algoritmi su evaluirani na različitim skupovima podataka, različitih veličina odnosno kompleksnosti. Za usporedbu algoritama, korištene su tipične metrike performansi koje se koriste u području strojnog učenja te je korištena i rigorozna statistička metodologija za usporedbu algoritama. Provedeni su eksperimenti u različitim scenarijima na različitim skupovima podataka čime smo došli do osnovnih zaključaka. Performanse svih metoda korištenih u radu uvelike ovise o scenariju problema. Promatrane metode daju najbolje rezultate u prvom najjednostavnijem scenariju, rekombiniranju lijekova. Prediktivna moć naučenih modela značajno je niža u scenarijima u kojem se model testira na novim lijekovima ili novim ciljanim molekulama.
Abstract (english)
In this thesis drug-target interaction problem has been analyzed. Scenarios for modelling drug target interaction problem have been introduced, as well as state-of-the-art machine learning algorithms. The goal of the thesis was to evaluate different approaches in context of said different interaction’s prediction scenarios. Algorithms were evaluated at benchmark datasets of different sizes and complexity. For comparison of algorithms, typical machine learning metrics were used as well as rigorous statistical methodology for algorithm comparison. By executing experiments in different scenarios on different datasets, we came to basic conclusions. Performances of all the methods used in the thesis depend largely on problem’s scenario. All observed methods give best results in the simplest scenario, drug repositioning. Predictive power of learned models is significantly lower in scenarios were new drugs or new targets were introduced.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:074538
Study programmeTitle: Computer Science and Mathematics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra računarstva i matematike (magistar/magistra računarstva i matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2021-03-23 12:21:28