Semantically improved genome-wide prediction of Gene Ontology annotations

Genomic annotations describing structural and functional features of genes and gene products through controlled terminologies and ontologies are extremely valuable, especially for computational analyses aimed at inferring new biomedical knowledge, which rely on available annotations. Yet, they are incomplete, especially for recently studied genomes, and only some of available annotations represent highly reliable human curated information. In order to help and speedup the time-consuming curation process and improve available annotations, computational methods able to provide prioritized lists of predicted annotations are paramount. Starting from a previous work on automatic prediction of Gene Ontology annotations based on singular value decomposition (SVD) of gene-to-term annotation matrix, here we propose a novel prediction algorithm that incorporates gene clustering based on gene functional similarity computed on Gene Ontology annotations. We tested both prediction methods performing k-fold cross-validation on two organism genomes, Saccharomyces cerevisiae (SGD) and Drosophila melanogaster (FlyBase). Results demonstrate effectiveness of our approach.