prikaz prve stranice dokumenta Clustering proteinskih poravnanja
  Download
PDF 601.31 KB
master's thesis
Clustering proteinskih poravnanja
Zagreb: University of Zagreb, Faculty of Science, 2024. urn:nbn:hr:217:771064
Buljan, Milan
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

Cite this document

Buljan, M. (2024). Clustering proteinskih poravnanja (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:771064

Buljan, Milan. "Clustering proteinskih poravnanja." Master's thesis, University of Zagreb, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:217:771064

Buljan, Milan. "Clustering proteinskih poravnanja." Master's thesis, University of Zagreb, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:217:771064

Buljan, M. (2024). 'Clustering proteinskih poravnanja', Master's thesis, University of Zagreb, Faculty of Science, accessed 23 December 2024, https://urn.nsk.hr/urn:nbn:hr:217:771064

Buljan M. Clustering proteinskih poravnanja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2024 [cited 2024 December 23] Available at: https://urn.nsk.hr/urn:nbn:hr:217:771064

M. Buljan, "Clustering proteinskih poravnanja", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:217:771064

Metadata
TitleClustering proteinskih poravnanja
AuthorMilan Buljan
MentorPavle Goldstein (mentor)
Committee memberPavle Goldstein (predsjednik povjerenstva)
Committee memberHrvoje Planinić (član povjerenstva)
Committee memberMiljenko Marušić (član povjerenstva)
Committee memberJosip Tambača (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2024-11-28, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
U ovom diplomskom radu promatrane su četiri proteinske familije: kinaze, ciklaze, acil transferaze (AT) i MLDH. Problem koji se obrađuje u ovom radu vezan je za pronalazak značajnih pozicija u proteinskim poravnanjima unutar pojedine proteinske familije, a koje su odlučujuće u klasifikaciji tih proteina na dvije podfamilije. Pripadnost podfamilijima u ovom radu nam je bila poznata, cilj je bio dobiti rangiranje pozicija prema vrijednostima \(F\)-faktora, a njegovo računanje vršili smo na tri različita načina. Primjenom dvije od te tri metode niz dobijenih \(F\)-faktora bio je \(F\) distribuiran, dok u trećoj nismo dobijali \(F\) distribuiranost podataka, ali ipak su proizlazili slični zaključci. Kroz rad je opisano kako smo sa niza aminokiselina prešli u vektorski prostor i koji je pristup korišten u pripremi podataka za obradu. U konačnici, usporedbom s dosadašnjim istraživanjima utvrđene su brojne sličnosti, ali i neka nova zapažanja, kao i drugačija rangiranja. Također, istaknuli smo pozicije koje su značajne s vjerojatnostima od 99\%, 95\% i 90\%. Prikazom na t-SNE grafovima utvrđeno je kako smo promatranjem top \(10\) pozicija prema rangiranju dobijali jasne separacije sa svega nekoliko krivo pridruženih proteina.
Abstract (english)
In this thesis, four protein families were studied: kinases, cyclases, acyl transferases (AT), and MLDH. The primary focus of this work is identifying significant positions in protein alignments within each protein family that are crucial for classifying these proteins into two subfamilies. The subfamily membership was known in advance, and the goal was to rank the positions based on F-factor values, calculated using three different methods. Using two of these methods, the resulting F-factors followed an F-distribution, while in the third method, the F-distribution was not observed; however, similar conclusions were still reached. The thesis describes the process of transforming amino acid sequences into a vector space and the approach used for preparing the data for analysis. Finally, a comparison with previous research revealed numerous similarities, as well as some new observations and different rankings. We also highlighted positions that are significant with probabilities of \(99\%\), \(95\%\), and \(90\%\). Visualization using t-SNE graphs showed that by focusing on the top \(10\) positions according to the rankings, clear separations were achieved, with only a few proteins misclassified.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:771064
Study programmeTitle: Mathematical Statistics
Study programme type: university
Study level: graduate
Academic / professional title: sveučilišni magistar matematike (sveučilišni magistar matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-11-11 18:36:17