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master's thesis
Predviđanje protein-protein interakcija iz primarne strukture putem "Random Forest" klasifikatora
Zagreb: University of Zagreb, Faculty of Science, 2010. urn:nbn:hr:217:365613
Franke, Vedran
University of Zagreb
Faculty of Science
Department of Biology

Institutional repository: Repository of the Faculty of Science

Cite this document

Franke, V. (2010). Predviđanje protein-protein interakcija iz primarne strukture putem "Random Forest" klasifikatora (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:365613

Franke, Vedran. "Predviđanje protein-protein interakcija iz primarne strukture putem "Random Forest" klasifikatora." Master's thesis, University of Zagreb, Faculty of Science, 2010. https://urn.nsk.hr/urn:nbn:hr:217:365613

Franke, Vedran. "Predviđanje protein-protein interakcija iz primarne strukture putem "Random Forest" klasifikatora." Master's thesis, University of Zagreb, Faculty of Science, 2010. https://urn.nsk.hr/urn:nbn:hr:217:365613

Franke, V. (2010). 'Predviđanje protein-protein interakcija iz primarne strukture putem "Random Forest" klasifikatora', Master's thesis, University of Zagreb, Faculty of Science, accessed 04 November 2024, https://urn.nsk.hr/urn:nbn:hr:217:365613

Franke V. Predviđanje protein-protein interakcija iz primarne strukture putem "Random Forest" klasifikatora [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2010 [cited 2024 November 04] Available at: https://urn.nsk.hr/urn:nbn:hr:217:365613

V. Franke, "Predviđanje protein-protein interakcija iz primarne strukture putem "Random Forest" klasifikatora", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2010. Available at: https://urn.nsk.hr/urn:nbn:hr:217:365613

Metadata
TitlePredviđanje protein-protein interakcija iz primarne strukture putem "Random Forest" klasifikatora
Title (english)Prediction of protein-protein interactions from primary structure using a Random Forest classifier
AuthorVedran Franke
MentorKristian Vlahoviček (mentor)
Committee memberKristian Vlahoviček (predsjednik povjerenstva)
Committee memberŽeljka Vidaković Cifrek (član povjerenstva)
Committee memberIta Gruić-Sovulj (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Biology)
Zagreb
Defense date and country2010, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Biology
Abstract
Međusobne interakcije između proteina temelj su niza bioloških procesa, od regulacije metaboličkih puteva, specifičnosti imunoloških reakcija, replikacije DNK do sinteze proteina. Nagli razvoj visokoprotočnih metoda doveo je do velikog povećanja produkcije bioloških sekvenci, stvorivši potrebu za razvojem metoda i alata za njihovu funkcijsku analizu, te predviđanje fenotipskih svojstava, kako na molekularnoj, tako i na razini cijelog organizma. U ovom radu smo agregirali strukturalne podatke iz postojećih baza podataka, čime smo dobili skup proteinskih kvaternih struktura visoke kvalitete koji nam je omogućio primjenu metoda strojnog učenja za predviđanje interakcija između proteina. Iskoristili smo „Random Forest“ algoritam za predviđanje interakcijskih aminokiselina iz primarnih struktura proteina. Pokazali smo da, iako „Random Forest“ alogritam ima mogućnost klasifikacije visokodimenzionalnih podataka s izuzetnom točnošću, trenutno znanje o strukturalnim faktorima koji utječu na specifičnost interakcija između proteina nije na razini koja bi omogućila predviđanje interakcija na razlučivosti pojedinih aminokiselina koristeći isključivo sekvence proteina.
Abstract (english)
The interaction between proteins is fundamental to a broad spectrum of biological functions, including regulation of metabolic pathways, immunological recognition, DNA replication, progression through the cell cycle, and protein synthesis. Due to the growing disparity between the amount of sequenced genomic content and functional data, there exist a pressing need for tools and methods that will enable prediction of phenotypic traits, on the molecular or organism level, based on the sequence alone. In this work we have constructed a high quality dataset of protein structures that has enabled us to use the Random Forest non-linear classificator to develop a method for prediction of interacting residues from the protein primary structure. Our results have shown that, although the Random Forest algorithm has a unique capability of accurately classifying highly dimensional data, we still have an incomplete knowledge of structural factors that determine the specificity of protein-protein interactions, thus putting an upper limit the on the usefulness of the machine learning approach in predicting protein interactions on the level of single amino-acids.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:365613
Study programmeTitle: Biology; specializations in: Molecular biology
Course: Molecular biology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra biologije (magistar/magistra biologije)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2018-03-23 10:30:55