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 438195
Title Gene Ontology consistent protein function prediction: the FALCON algorithm applied to six eukaryotic genomes
Author(s) Kourmpetis, Y.A.I.; Dijk, A.D.J. van; Braak, C.J.F. ter
Source Algorithms for Molecular Biology 8 (2013)1. - ISSN 1748-7188
DOI http://dx.doi.org/10.1186/1748-7188-8-10
Department(s) Biometris (WU MAT)
PRI BIOS Applied Bioinformatics
Biometris (PPO/PRI)
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2013
Keyword(s) arabidopsis-thaliana - integration - annotation - regression - network - classification - association - terms - tool
Abstract Gene Ontology (GO) is a hierarchical vocabulary for the description of biological functions and locations, often employed by computational methods for protein function prediction. Due to the structure of GO, function predictions can be self- contradictory. For example, a protein may be predicted to belong to a detailed functional class, but not in a broader class that, due to the vocabulary structure, includes the predicted one.We present a novel discrete optimization algorithm called Functional Annotation with Labeling CONsistency (FALCON) that resolves such contradictions. The GO is modeled as a discrete Bayesian Network. For any given input of GO term membership probabilities, the algorithm returns the most probable GO term assignments that are in accordance with the Gene Ontology structure. The optimization is done using the Differential Evolution algorithm. Performance is evaluated on simulated and also real data from Arabidopsis thaliana showing improvement compared to related approaches. We finally applied the FALCON algorithm to obtain genome-wide function predictions for six eukaryotic species based on data provided by the CAFA (Critical Assessment of Function Annotation) project
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