Anomaly-free Prediction of Gene Ontology Annotations Using Bayesian Networks

Gene and protein structural and functional annotations expressed through controlled terminologies and ontologies are paramount especially for the aim of inferring new biomedical knowledge through computational analyses. However, the available annotations are incomplete, in particular for recently studied genomes, and only a few of them are highly reliable human curated information. To support and speed up the time-consuming curation process, prioritized lists of computationally predicted annotations are hence extremely useful. In this paper we leverage a previous work on the automatic prediction of Gene Ontology annotations based on the singular value decomposition (SVD) of the gene-to-term annotation matrix, and we propose a novel post-processing method that uses a Bayesian network to eliminate predictions of anomalous annotations. In fact, we observed that the predicted annotation profiles might suggest that a gene shall be annotated to a term, but not to one of its ancestors, thus...

• Type: Conference
• Year: 2009
• Where: BIBE
• Authors: Marco Tagliasacchi, Marco Masseroli
• Keywords: Annotations, BIBE 2009, Bioinformatics, Gene Ontology, Gene Ontology Annotations