Projects per year
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.
|Qualification||Doctor of Philosophy|
|Award date||4 Oct 2011|
|Place of Publication||[S.l.]|
|Publication status||Published - 2011|
- bayesian theory
- markov processes
- network analysis
- applied statistics
- molecular biology
FingerprintDive into the research topics of 'Bayesian Markov random field analysis for integrated network-based protein function prediction'. Together they form a unique fingerprint.
- 1 Finished
Comparative genomics in computational prediction of gene function and regulation: Bayesian gene function prediction
Kourmpetis, Y., ter Braak, C. & van Ham, R.
2/01/06 → 4/10/11