Prediction of candidate primary immunodeficiency disease genes using a support vector machine learning approach

DNA Research

Volumn 16, Issue 6, 2009, Pages 345-351

  Keerthikumar, Shivakumar ^a,b,c   Bhadra, Sahely ^d   Kandasamy, Kumaran ^a,b,e   Raju, Rajesh ^a,b,c   Ramachandra, Y L ^b   Bhattacharyya, Chiranjib ^d   Imai, Kohsuke ^h   Ohara, Osamu ^f,g   Mohan, Sujatha ^a,c   Pandey, Akhilesh ^a,e  

^a Institute of Bioinformatics   (India)

^b KUVEMPU UNIVERSITY   (India)

^c RIKEN YOKOHAMA INSTITUTE   (Japan)

^d INDIAN INSTITUTE OF SCIENCE   (India)

^e JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^f Yokohama Institute   (Japan)

^g KAZUSA DNA RESEARCH INSTITUTE   (Japan)

^h NATIONAL DEFENSE MEDICAL COLLEGE   (Japan)

Author keywords

HPRD; Human Proteinpedia; NetPath; RAPID; SVM

Indexed keywords

ALGORITHM; ARTICLE; CONTROLLED STUDY; GENE EXPRESSION; HUMAN; IMMUNE DEFICIENCY; MICROARRAY ANALYSIS; PREDICTION; PROTEIN PROTEIN INTERACTION; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; ASIA; BIOLOGY; EVALUATION; GENETIC DATABASE; GENETIC PREDISPOSITION; GENETICS; METABOLISM; METHODOLOGY; PREDICTION AND FORECASTING;

PROTEIN;

ALGORITHMS; ASIA; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; GENETIC PREDISPOSITION TO DISEASE; HUMANS; IMMUNOLOGIC DEFICIENCY SYNDROMES; PREDICTIVE VALUE OF TESTS; PROTEINS; SENSITIVITY AND SPECIFICITY;

EID: 75349087138     PISSN: 13402838     EISSN: 17561663     Source Type: Journal    
DOI: 10.1093/dnares/dsp019     Document Type: Article

References (66)

1. Geha, R.S., Notarangelo, L.D., Casanova, J.L., et al. 2007, Primary immunodeficiency diseases: an update from the International Union of Immunological Societies Primary Immunodeficiency Diseases Classification Committee, J. Allergy Clin. Immunol., 120, 776-94.
2. Marodi, L. and Notarangelo, L.D. 2007, Immunological and genetic bases of new primary immunodeficiencies, Nat. Rev. Immunol., 7, 851-61.
3. Brown, M.P., Grundy, W.N., Lin, D., et al. 2000, Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc. Natl Acad. Sci. USA, 97, 262-7.
4. Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M. and Haussler, D. 2000, Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, 16, 906-14.
5. Pirooznia, M., Yang, J.Y., Yang, M.Q. and Deng, Y. 2008, A comparative study of different machine learning methods on microarray gene expression data, BMC Genomics, 9, Suppl 1, S13.
6. Wang, L., Zhu, J. and Zou, H. 2008, Hybrid huberized support vector machines for microarray classification and gene selection, Bioinformatics, 24, 412-9.
7. Wang, Y., Tetko, I.V., Hall, M.A., et al. 2005, Gene selection from microarray data for cancer classification - A machine learning approach, Comput. Biol. Chem., 29, 37-46.
8. Yeang, C.H., Ramaswamy, S., Tamayo, P., et al. 2001, Molecular classification of multiple tumor types, Bioinformatics, 17, Suppl 1, S316-22.
9. Hua, S. and Sun, Z. 2001, A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach, J. Mol. Biol., 308, 397-407.
10. Jagla, B. and Schuchhardt, J. 2000, Adaptive encoding neural networks for the recognition of human signal peptide cleavage sites, Bioinformatics, 16, 245-50.
11. Zien, A., Ratsch, G., Mika, S., Scholkopf, B., Lengauer, T. and Muller, K.R. 2000, Engineering support vector machine kernels that recognize translation initiation sites, Bioinformatics, 16, 799-807.
12. Cai, Y.D., Liu, X.J., Xu, X. and Zhou, G.P. 2001, Support vector machines for predicting protein structural class, BMC Bioinformatics, 2, 3.
13. Ding, C.H. and Dubchak, I. 2001, Multi-class protein fold recognition using support vector machines and neural networks, Bioinformatics, 17, 349-58.
14. Bock, J.R. and Gough, D.A. 2001, Predicting protein-protein interactions from primary structure, Bioinformatics, 17, 455-60.
15. Bhasin, M. and Raghava, G.P. 2004, ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST, Nucleic Acids Res., 32, W414-9.
16. Garg, A., Bhasin, M. and Raghava, G.P. 2005, Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search, J. Biol. Chem., 280, 14427-32.
17. Hua, S. and Sun, Z. 2001, Support vector machine approach for protein subcellular localization prediction, Bioinformatics, 17, 721-8.
18. Shi, J.Y., Zhang, S.W., Pan, Q., Cheng, Y.M. and Xie, J. 2007, Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition, Amino Acids, 33, 69-74.
19. Anderson, D.C., Li, W., Payan, D.G. and Noble, W.S. 2003, A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores, J. Proteome. Res., 2, 137-46.
20. Park, K.J., Gromiha, M.M., Horton, P. and Suwa, M. 2005, Discrimination of outer membrane proteins using support vector machines, Bioinformatics, 21, 4223-9.
21. Keerthikumar, S., Raju, R., Kandasamy, K., et al. 2009, RAPID: resource of Asian primary immunodeficiency diseases, Nucleic Acids Res., 37, D863-7.
22. Kanehisa, M., Araki, M., Goto, S., et al. 2008, KEGG for linking genomes to life and the environment, Nucleic Acids Res., 36, D480-4.
23. Kanehisa, M. and Goto, S. 2000, KEGG: Kyoto encyclopedia of genes and genomes, Nucleic Acids Res., 28, 27-30.
24. Kanehisa, M., Goto, S., Hattori, M., et al. 2006, From genomics to chemical genomics: new developments in KEGG, Nucleic Acids Res., 34, D354-7.
25. Hijikata, A., Kitamura, H., Kimura, Y., et al. 2007, Construction of an open-access database that integrates cross-reference information from the transcrip-tome and proteome of immune cells, Bioinformatics, 23, 2934-41.
26. Keshava Prasad, T.S., Goel, R., Kandasamy, K., et al. 2009, Human protein reference database - 2009 update, Nucleic Acids Res., 37, D767-72.
27. Kandasamy, K., Keerthikumar, S., Goel, R., et al. 2009, Human Proteinpedia: a unified discovery resource for proteomics research, Nucleic Acids Res., 37, D773-81.
28. Blake, J.A., Bult, C.J., Eppig, J.T., Kadin, J.A. and Richardson, J.E. 2009, The mouse genome database genotypes: phenotypes, Nucleic Acids Res., 37, D712-9.
29. Bult, C.J., Eppig, J.T., Kadin, J.A., Richardson, J.E. and Blake, J.A. 2008, The mouse genome database (MGD): mouse biology and model systems, Nucleic Acids Res., 36, D724-8.
30. Eppig, J.T., Blake, J.A., Bult, C.J., Kadin, J.A. and Richardson, J.E. 2007, The mouse genome database (MGD): new features facilitating a model system, Nucleic Acids Res., 35, D630-7.
31. Amberger, J., Bocchini, C.A., Scott, A.F. and Hamosh, A. 2009, McKusick's online Mendelian inheritance in man (OMIM), Nucleic Acids Res., 37, D793-6.
32. Botstein, D. and Risch, N. 2003, Discovering genotypes underlying human phenotypes: past successes for Mendelian disease, future approaches for complex disease, Nat. Genet., 33, Suppl, 228-37.
33. Glazier, A.M., Nadeau, J.H. and Aitman, T.J. 2002, Finding genes that underlie complex traits, Science, 298, 2345-9.
34. Freudenberg, J. and Propping, P. 2002, A similarity-based method for genome-wide prediction of disease-relevant human genes, Bioinformatics, 18, Suppl 2, S110-5.
35. Huang, D. and Chow, T.W. 2007, Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer, Bioinformatics, 23, 1503-10.
36. Kohler, S., Bauer, S., Horn, D. and Robinson, P.N. 2008, Walking the interactome for prioritization of candidate disease genes, Am. J. Hum. Genet., 82, 949-58.
37. Perez-Iratxeta, C., Bork, P. and Andrade, M.A. 2002, Association of genes to genetically inherited diseases using data mining, Nat. Genet., 31, 316-9.
38. Segal, E., Wang, H. and Koller, D. 2003, Discovering molecular pathways from protein interaction and gene expression data, Bioinformatics, 19, Suppl 1, i264-71.
39. Wang, K., Li, M. and Bucan, M. 2007, Pathway-based approaches for analysis of genomewide association studies, Am. J. Hum. Genet., 81, 1278-1283.
40. Zhang, H.H., Ahn, J., Lin, X. and Park, C. 2006, Gene selection using support vector machines with non-convex penalty, Bioinformatics, 22, 88-95.
41. Lewis, D.P., Jebara, T. and Noble, W.S. 2006, Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure, Bioinformatics, 22, 2753-60.
42. Radivojac, P., Peng, K., Clark, W.T., et al. 2008, An integrated approach to inferring gene-disease associations in humans, Proteins, 72, 1030-7.
43. Boztug, K., Appaswamy, G., Ashikov, A., et al. 2009, A syndrome with congenital neutropenia and mutations in G6PC3, N. Engl. J. Med., 360, 32-43.
44. van der Burg, M., Ijspeert, H., Verkaik, N.S., et al. 2009, A DNA-PKcs mutation in a radiosensitive T-B-SCID patient inhibits Artemis activation and nonhomologous end-joining, J. Clin. Invest., 119, 91-8.
45. von Bernuth, H., Picard, C., Jin, Z., et al. 2008, Pyogenic bacterial infections in humans with MyD88 deficiency, Science, 321, 691-6.
46. Aksentijevich, I., Masters, S.L., Ferguson, P.J., et al. 2009, An autoinflammatory disease with deficiency of the interleukin-1-receptor antagonist, N. Engl. J. Med., 360, 2426-37.
47. Huck, K., Feyen, O., Niehues, T., et al. 2009, Girls homo-zygous for an IL-2-inducible T cell kinase mutation that leads to protein deficiency develop fatal EBV-associated lymphoproliferation, J. Clin. Invest., 119, 1350-8.
48. Reddy, S., Jia, S., Geoffrey, R., et al. 2009, An autoinflam-matory disease due to homozygous deletion of the IL1RN locus, N. Engl. J. Med., 360, 2438-44.
49. Shiow, L.R., Roadcap, D.W., Paris, K., et al. 2008, The actin regulator coronin 1A is mutant in a thymic egress-deficient mouse strain and in a patient with severe combined immunodeficiency, Nat. Immunol., 9, 1307-15.
50. Kenny, E.F. and O'Neill, L.A. 2008, Signalling adaptors used by toll-like receptors: an update, Cytokine, 43, 342-9.
51. Chen, J., Aronow, B.J. and Jegga, A.G. 2009, Disease candidate gene identification and prioritization using protein interaction networks, BMC Bioinformatics, 10, 73.
52. Ortutay, C. and Vihinen, M. 2009, Identification of candidate disease genes by integrating Gene Ontologies and protein-interaction networks: case study of primary immunodeficiencies, Nucleic Acids Res., 37, 622-8.
53. Bader, G.D., Betel, D. and Hogue, C.W. 2003, BIND: the biomolecular interaction network database, Nucleic Acids Res., 31, 248-50.
54. Breitkreutz, B.J., Stark, C., Reguly, T., et al. 2008, The BioGRID interaction database: 2008 update, Nucleic Acids Res., 36, D637-40.
55. Harley, J.B., Alarcon-Riquelme, M.E., Criswell, L.A., et al. 2008, Genome-wide association scan in women with systemic lupus erythematosus identifies susceptibility variants in ITGAM, PXK, KIAA1542 and other loci, Nat. Genet., 40, 204-10.
56. Nath, S.K., Han, S., Kim-Howard, X., et al. 2008, A nonsynonymous functional variant in integrin-alpha(M) (encoded by ITGAM) is associated with systemic lupus erythematosus, Nat. Genet., 40, 152-4.
57. Kozyrev, S.V., Abelson, A.K., Wojcik, J., et al. 2008, Functional variants in the B-cell gene BANK1 are associated with systemic lupus erythematosus, Nat. Genet., 40, 211-6.
58. Goyette, P., Lefebvre, C., Ng, A., et al. 2008, Gene-centric association mapping of chromosome 3p implicates MST1 in IBD pathogenesis, Mucosal Immunol., 1, 131-8.
59. Johnson, A.D. and O'Donnell, C.J. 2009, An open access database of genome-wide association results, BMC Med. Genet., 10, 6.
60. Wellcome Trust Case Control Consortium 2007, Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls, Nature, 447, 661-78.
61. Todd, J.A., Walker, N.M., Cooper, J.D., et al. 2007, Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes, Nat. Genet., 39, 857-64.
62. Plenge, R.M., Cotsapas, C., Davies, L., et al. 2007, Two independent alleles at 6q23 associated with risk of rheumatoid arthritis, Nat. Genet., 39, 1477-82.
63. Remmers, E.F., Plenge, R.M., Lee, A.T., et al. 2007, STAT4 and the risk of rheumatoid arthritis and systemic lupus erythematosus, N. Engl. J. Med., 357, 977-86.
64. Graham, D.S., Graham, R.R., Manku, H., et al. 2008, Polymorphism at the TNF superfamily gene TNFSF4 confers susceptibility to systemic lupus erythematosus, Nat. Genet., 40, 83-9.
65. Ueda, H., Howson, J.M., Esposito, L., et al. 2003, Association of the T-cell regulatory gene CTLA4 with susceptibility to autoimmune disease, Nature, 423, 506-11.
66. Ikegami, H., Awata, T., Kawasaki, E., et al. 2006, The association of CTLA4 polymorphism with type 1 diabetes is concentrated in patients complicated with autoimmune thyroid disease: a multicenter collaborative study in Japan, J. Clin. Endocrinol. Metab., 91, 1087-92.