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A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks
Nov.-Dec. 2012 (vol. 9 no. 6)
pp. 1812-1818
M. Re, Dipt. di Inf., Univ. degli Studi di Milano, Milan, Italy
M. Mesiti, Dipt. di Inf., Univ. degli Studi di Milano, Milan, Italy
G. Valentini, Dipt. di Inf., Univ. degli Studi di Milano, Milan, Italy
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 significantly better results than the compared state-of-the-art network-based algorithms for gene function prediction, and with relevant savings in computational time. The proposed approach is general and fast enough to be in perspective applied to other relevant node ranking problems in large and complex biological networks.
Index Terms:
topology,biology computing,cellular biophysics,computational complexity,genetics,microorganisms,molecular biophysics,large biological networks,fast ranking algorithm,functional networks,biological function,computational complexity problems,transductive gene ranking method,kernelized score functions,topology,graph structure,multiple biomolecular data sources,yeast model organism,state-of-the-art network-based algorithms,gene function prediction,relevant node ranking problems,complex biological networks,Symmetric matrices,Proteins,Bioinformatics,Hilbert space,Prediction algorithms,kernel functions,Gene function prediction,gene ranking,biological networks
Citation:
M. Re, M. Mesiti, G. Valentini, "A Fast Ranking Algorithm for Predicting Gene Functions in Biomolecular Networks," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 6, pp. 1812-1818, Nov.-Dec. 2012, doi:10.1109/TCBB.2012.114
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