Statistički aspekti pretraživanja proteoma

master's thesis

• Author: Iva Sorić
• Mentor: Pavle Goldstein (mentor)
• Committee member: Pavle Goldstein (predsjednik povjerenstva)
• Committee member: Boris Guljaš (član povjerenstva)
• Committee member: Nela Bosner (član povjerenstva)
• Committee member: Marko Vrdoljak (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Mathematics); Zagreb
• Defense date and country: 2015-09-23, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Mathematics

Abstract

Opisan je razvoj skrivenog Markovljevog modela za traženje motiva karakterističnih za članove određene proteinske familije. Nakon analize “score”-ova dobivenih na simuliranim proteomima konstruirana je korekcija tako da korigirani “score”-ovi služe kao kriterij za identifikaciju nizova koji sadrže tražene motive. Metoda je primijenjena na četiri biljna proteoma s pozitivnim rezultatima.

Abstract (english)

In this work we study one of the methods used for identifying proteins characterized by specific motifs. We have described a hidden Markov model designed for searching for motifs. After analyzing the scores obtained on simulated data, we have constructed a score correction that should yield a valid criterion for discriminating sequences with wanted motifs. In the end we apply this method to four plant proteomes with positive results.

• Keywords: skriveni Markovljev model; traženje motiva karakterističnih za članove određene proteinske familije; biljni proteomi
• Keywords (english): hidden Markov model; searching for motifs; plant proteomes
• Language: croatian
• URN:NBN: urn:nbn:hr:217:464736
• Study programme: Title: Mathematical Statistics; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2019-02-07 11:27:07