Network-based gene function prediction through kernelized score functions

Ranking genes in functional networks according to a specific biological function is a challenging task raising relevant performance and computational complexity problems.

To cope with both these problems we developed a transductive gene ranking method based on kernelized score functions able to fully exploit the topology and the graph structure of biomolecular networks and to capture significant functional relationships between genes.

We run the method on a network constructed by integrating multiple biomolecular data sources in the yeast model organism, achieving results at least comparable with state-of-the-art network-based algorithms for gene function prediction (Re, Mesiti and Valentini, 2012).

An automated pipeline for protein function prediction, that adopts the kernelized score functions to rank genes in a multi-species setting, has been developed for the CAFA2 challenge (Re, Mesiti and Valentini, 2014).


M. Re, M.Mesiti, G. Valentini, An automated pipeline for multi-species protein function prediction from the UniProt Knowledgebase, Automated Function Prediction SIG 2014 - ISMB 2014, Boston, USA

M. Re, M. Mesiti and G. Valentini, A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks, IEEE ACM Transactions on Computational Biology and Bioinformatics 9(6) pp. 1812-1818, 2012. IEEE link