Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 409857
Title Bayesian Markov random field analysis for integrated network-based protein function prediction
Author(s) Kourmpetis, Y.I.A.
Source University. Promotor(en): Cajo ter Braak, co-promotor(en): Roeland van Ham. - [S.l.] : S.n. - ISBN 9789085859598 - 113
Department(s) Bioinformatics
Biometris (PPO/PRI)
PRI BIOS Applied Bioinformatics
EPS-1
Publication type Dissertation, internally prepared
Publication year 2011
Keyword(s) statistiek - bayesiaanse theorie - markov-processen - netwerkanalyse - biostatistiek - toegepaste statistiek - bio-informatica - eiwitten - genen - moleculaire biologie - statistics - bayesian theory - markov processes - network analysis - biostatistics - applied statistics - bioinformatics - proteins - genes - molecular biology
Categories Mathematical Statistics / Bioinformatics (General)
Abstract

Unravelling the functions of proteins is one of the most important aims of modern biology. Experimental inference of protein function is expensive and not scalable to large datasets. In this thesis a probabilistic method for protein function prediction is presented that integrates different types of data such as sequences and networks. The method is based on Bayesian Markov Random Field (BMRF) analysis. BMRF was initially applied to genome wide protein function prediction using network data in yeast and in also in Arabidopsis by integrating protein domains (i.e InterPro signatures), expressions and protein protein interactions. Several of the predictions were confirmed by experimental evidence. Further, an evolutionary discrete optimization algorithm is presented that integrates function predictions from different Gene Ontology (GO) terms to a single prediction that is consistent to the True Path Rule as imposed by the GO Directed Acyclic Graph. This integration leads to predictions that are easy to be interpreted. Evaluation of of this algorithm using Arabidopsis data showed that the prediction performance is improved, compared to single GO term predictions.

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