Abstract
Identifying disease genes is essential for elucidating pathogenesis and developing diagnosis and prevention measures. We have developed a computational tool, named DGFinder, to assess candidate genes in interested chromosome regions for their possibility relating to a given disease. DGFinder prioritizes the candidate genes based on a new approach to measure the functional similarity to the known causative genes of the disease. The performance of DGFinder was evaluated with a dataset containing 1045 genes related to 305 diseases. The validation results showed that 16.1% and 56.7% of disease-associated genes were at the top 1 and top 5 of the list prioritized by DGFinder. Therefore, DGFinder can effectively help the selection of candidate genes in interested chromosome regions for mutation analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lander, E.S., et al.: Initial sequencing and analysis of the human genome. Nature, 860–921 (2001)
Venter, J.C., et al.: The sequence of the human genome. Science, 1304–1351 (2001)
Risch, N.J.: Searching for genetic determinants in the new millennium. Nature, 847–856 (2000)
Lopez-Bigas, N., Ouzounis, C.A.: Genome-wide identification of genes likely to be involved in human genetic disease. Nucleic Acids Res., 3108–3114 (2004)
Adie, E.A., et al.: Speeding disease gene discovery by sequence based candidate prioritization. BMC Bioinformatics (2005)
Kondrashov, F.A., Ogurtsov, A.Y., Kondrashov, A.S.: Bioinformatical assay of human gene morbidity. Nucleic Acids Res, 1731-1737 (2004)
Freudenberg, J., Propping, P.: A similarity-based method for genome-wide prediction of disease-relevant human genes. Bioinformatics (2002)
Perez-Iratxeta, C., Bork, P., Andrade, M.A.: Association of genes to genetically inherited diseases using data mining. Nat. Genet., 316–319 (2002)
Perez-Iratxeta, C., Bork, P., Andrade-Navarro, M.A.: Update of the G2D tool for prioritization of gene candidates to inherited diseases. Nucleic Acids Res. (2007)
Perez-Iratxeta, C., et al.: G2D: a tool for mining genes associated with disease. BMC Genet. (2005)
Turner, F.S., Clutterbuck, D.R., Semple, C.A.: POCUS: mining genomic sequence annotation to predict disease genes. Genome Biol. (2003)
van Driel, M.A., et al.: A new web-based data mining tool for the identification of candidate genes for human genetic disorders. Eur. J. Hum. Genet., 57–63 (2003)
van Driel, M.A., et al.: GeneSeeker: extraction and integration of human disease-related information from web-based genetic databases. Nucleic Acids Res. (2005)
Xu, J., Li, Y.: Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics, 2800–2805 (2006)
Oti, M., et al.: Predicting disease genes using protein-protein interactions. J. Med. Genet. 43(8), 691–698 (2006)
George, R.A., et al.: Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res. (2006)
Adie, E.A., et al.: SUSPECTS: enabling fast and effective prioritization of positional candidates. Bioinformatics, 773–774 (2006)
Aerts, S., et al.: Gene prioritization through genomic data fusion. Nat. Biotechnol., 537–544 (2006)
Franke, L., et al.: Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet., 1011–1025 (2006)
Calvo, B., et al.: A partially supervised classification approach to dominant and recessive human disease gene prediction. Comput. Methods Programs Biomed., 229–237 (2007)
Huang, Q.Y., Li, G.H.Y., Cheung, W.M.W., et al.: Prediction of osteoporosis candidate genes by computational disease-gene identification strategy. J. Hum. Genet., 644–655 (2008)
Jimenez, G., Childs, B., Valle, D.: Human disease genes. Nature, 853–855 (2001)
Badano, J.L., Katsanis, N.: Beyond Mendel: an evolving view of human genetic disease transmission. Nat. Rev. Genet., 779–789 (2002)
Goh, K.I., et al.: The human disease network. Proc. Natl. Acad. Sci. USA, 8685–8690 (2007)
Ashburner, M., et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet., 25–29 (2000)
Botstein, D., Risch, N.: Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat. Genet., 228–237 (2003)
Weller, S., Gould, S.J., Valle, D.: Peroxisome biogenesis disorders. Annu. Rev. Genomics Hum. Genet., 165–211 (2003)
Laakso, M., Kubaszek, A.: Candidate genes for insulin resistance: what’s new? International Congress Series, 55–61 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yuan, F., Wang, R., Guan, M., He, G. (2010). A Novel Computational Method for Predicting Disease Genes Based on Functional Similarity. In: Huang, DS., Zhang, X., Reyes GarcÃa, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-14932-0_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14931-3
Online ISBN: 978-3-642-14932-0
eBook Packages: Computer ScienceComputer Science (R0)