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Issue No.06 - Nov.-Dec. (2012 vol.9)
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
ABSTRACT
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
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
REFERENCES
[1] R. Sharan, I. Ulitsky, and R. Shamir, “Network-Based Prediction of Protein Function,” Molecular Systems Biology, vol. 3, article 88, 2007.
[2] R. Rentzsch and C. Orengo, “Protein Function Prediction-the Power of Multiplicity,” Trends Biotechnology, vol. 27, no. 4, pp. 210-219, 2009.
[3] A. Ruepp et al., “The FunCat, a Functional Annotation Scheme for Systematic Classification of Proteins from Whole Genomes,” Nucleic Acids Research, vol. 32, no. 18, pp. 5539-5545, 2004.
[4] S. Altschul et al., “Basic Local Alignment Search Tool,” J. Molecular Biology, vol. 215, pp. 403-410, 1990.
[5] P. Pavlidis et al., “Learning Gene Functional Classification from Multiple Data,” J. Computational Biology, vol. 9, pp. 401-411, 2002.
[6] M. Mayer and P. Hieter, “Protein Networks - Guilt by Association,” Nature Biotechnology, vol. 18, no. 12, pp. 1242-1243, 2000.
[7] A. Vazquez, A. Flammini, A. Maritan, and A. Vespignani, “Global Protein Function Prediction from Protein-Protein Interaction Networks,” Nature Biotechnology, vol. 21, pp. 697-700, 2003.
[8] H. Chua, W. Sung, and L. Wong, “An Efficient Strategy for Extensive Integration of Diverse Biological Data for Protein Function Prediction,” Bioinformatics, vol. 23, no. 24, pp. 3364-3373, 2007.
[9] A. Bertoni, M. Frasca, and G. Valentini, “COSNet: A Cost Sensitive Neural Network for Semi-Supervised Learning in Graphs,” Proc. European Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD '11), pp. 219-234, 2011.
[10] A. Mitrofanova, V. Pavlovic, and B. Mishra, “Prediction of Protein Functions with Gene Ontology and Interspecies Protein Homology Data,” IEEE/ACM Trans Computational Biology and Bioinformatics, vol. 8, no. 3, pp. 775-784, May/June 2011.
[11] M. Deng, T. Chen, and F. Sun, “An Integrated Probabilistic Model for Functional Prediction of Proteins,” J. Computational Biology, vol. 11, pp. 463-475, 2004.
[12] S. Mostafavi, D. Ray, D. Warde-Farley, C. Grouios, and Q. Morris, “GeneMANIA: A Real-Time Multiple Association Network Integration Algorithm for Predicting Gene Function,” Genome Biology, vol. 9, article S4, 2008.
[13] H. Kashima, K. Tsuda, and A. Inokuchi, “Kernels for Graphs,” Kernel Methods in Computational Biology, pp. 155-170, MIT Press, 2004.
[14] B. Scholkopf, K. Tsuda, and J. Vert, Kernel Methods in Computational Biology. MIT Press, 2004.
[15] G. Lippert, Z. Ghahramani, and K. Borgwardt, “Gene Function Prediction form Synthetic Leathality Networks via Ranking on Demand,” Bioinformatics, vol. 26, no. 7, pp. 912-918, 2010.
[16] A. Smola and I. Kondor, “Kernel and Regularization on Graphs,” Proc. Ann. Conf. COLT, pp. 144-158, 2003.
[17] L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank Citation Ranking: Bringing Order to the Web,” technical report, Stanford Digital Library Technologies Project, Stanford Univ., CA, Nov. 1998.
[18] G. Valentini, “True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 8, no. 3, pp. 832-847, May/June 2011.
[19] G. Valentini and N. Cesa-Bianchi, “Hcgene: A Software Tool to Support the Hierarchical Classification of Genes,” Bioinformatics, vol. 24, no. 5, pp. 729-731, 2008.
[20] L. Pena-Castillo et al., “A Critical Assessment of Mus Musculus Gene Function Prediction Using Integrated Genomic Evidence,” Genome Biology, vol. 9, article S1, 2008.
[21] L. Lovasz, “Random Walks on Graphs: A Survey,” Combinatorics, Paul Erdos Is Eighty, vol. 2, pp. 1-46, 1993.
[22] M. Re and G. Valentini, “Random Walking on Functional Interaction Networks to Rank Genes Involved in Cancer,” Proc. Second Artificial Intelligence Applications in Biomedicine Workshop, pp. 66-75, 2012.
[23] H. Lin, C. Lin, and R. Weng, “A Note on Platt's Probabilistic Outputs for Support Vector Machines,” Machine Learning, vol. 68, pp. 267-276, 2007.
[24] C. Chang and C. Lin, “LIBSVM : A Library for Support Vector Machines,” ACM Trans. Intelligent Systems and Technology, vol. 2, no. 3, pp. 27:1-27:27, 2011.
[25] N. Cesa-Bianchi, M. Re, and G. Valentini, “Synergy of Multi-Label Hierarchical Ensembles, Data Fusion, and Cost-Sensitive Methods for Gene Functional Inference,” Machine Learning, vol. 88, no. 1, pp. 209-241, 2012.
[26] Y. Bengio, O. Delalleau, and N. Le Roux, “Label Propagation and Quadratic Criterion,” Semi-Supervised Learning, pp. 193-216, MIT Press, 2006.
[27] M. Re and G. Valentini, “Cancer Module Genes Ranking Using Kernelized Score Functions,” BMC Bioinformatics, vol. 13 (Suppl 14), article S3, 2012.
[28] M. Re and G. Valentini, “Large Scale Ranking and Repositioning of Drugs with Respect to Drugbank Therapeutic Categories,” Proc. Eighth Int'l Conf. Bioinformatics Research and Applications (ISBRA '12), pp. 225-236, 2012.
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