EnsembleGASVR: A novel ensemble method for classifying missense single nucleotide polymorphisms

Bioinformatics

Volumn 30, Issue 16, 2014, Pages 2324-2333

  Rapakoulia, Trisevgeni ^a   Theofilatos, Konstantinos ^b   Kleftogiannis, Dimitrios ^a   Likothanasis, Spiros ^b   Tsakalidis, Athanasios ^b   Mavroudi, Seferina ^b,c  

^a KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

^b UNIVERSITY OF PATRAS   (Greece)

^c TECHNOLOGICAL EDUCATIONAL INSTITUTE OF WESTERN GREECE   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; AMINO ACID SUBSTITUTION; HUMAN; MISSENSE MUTATION; SINGLE NUCLEOTIDE POLYMORPHISM;

ALGORITHMS; AMINO ACID SUBSTITUTION; HUMANS; MUTATION, MISSENSE; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84906276572     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu297     Document Type: Article

References (57)

1. Abecasis, G.R. et al. (2010) A map of human genome variation from populationscale sequencing. Nature, 467, 1061-1073.
2. Acharya, V. and Nagarajaram, H.A. (2012) Hansa: an automated method for discriminating disease and neutral human nsSNPs. Hum. Mut., 33, 332-337.
3. Adzhubei, I. et al. (2010) A method and server for predicting damaging missense mutations. Nat. Methods, 7, 248-249.
4. Akbani, R. and Kwek, S. (2004) Applying support vector machines to imbalanced datasets. Lect. Notes Comput. Sci., 3201, 39-50.
5. Bell, J. (2004) Predicting disease using genomics. Nature, 429, 453-456.
6. Blom, N. et al. (1999) Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J. Mol. Biol., 294, 1351-1362.
7. Botta, E. et al. (1998) Analysis of mutations in the XPD gene in Italian patients with trichothiodystrophy: site of mutation correlates with repair deficiency, but gene dosage appears to determine clinical severity. Am. J. Hum. Genet., 63, 1036-1048.
8. Bromberg, Y. and Rost, B. (2007) SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res., 35, 3823-3835.
9. Calabrese, R. et al. (2009) Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum. Mutat., 30, 1237-1244.
10. Capriotti, E. et al. (2006) Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics, 22, 2729-2734.
11. Cargill, M. et al. (1999) Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat. Genet., 22, 231-238.
12. Castellana, S. and Mazza, T. (2013) Congruency in the prediction of pathogenic missense mutations: state-of-the-art web-based tools. Brief. Bioinform., 4, 448-459.
13. Chang, Y.I. (2003) Boosting SVM Classifiers with Logistic Regression. Technical report, Academia Sinica, 2003 [http://www3stat.sinica.edu.tw/library/ c-tec- rep/2003-03.pdf].
14. Cheng, J. et al. (2006) Prediction of protein stability changes for single-site mutations using support vector machines. Proteins, 62, 1125-1132.
15. Dosztányi, Z. et al. (2005) The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. J. Mol. Biol., 347, 827-839.
16. Espinós, C. et al. (2003) Molecular analyses in hemophilia B families: identification of six new mutations in the factor IX gene. Haematologica, 88, 235-236.
17. Furney, S.J. et al. (2006) Differences in the evolutionary history of disease genes affected by dominant or recessive mutations. BMC Genomics, 7, 165.
18. Giacomini, K.M. et al. (2007) The pharmacogenetics research network: from SNP discovery to clinical drug response. Clin. Pharmacol. Ther., 81, 328-345.
19. Giardine, B. et al. (2007) PhenCode: connecting ENCODE data with mutations and phenotype. Hum. Mut., 28, 554-562.
20. Goldstein, D.B. and Cavalleri, G.L. (2005) Genomics: understanding human diversity. Nature, 437, 1241-1242.
21. Guerrini, M.M. et al. (2008) Human osteoclast-poor osteopetrosis with hypogammaglobulinemia due to TNFRSF11A (RANK) mutations. Am. J.Hum. Genet., 83, 64-76.
22. Hart, P.S. et al. (2000) Identification of cathepsin C mutations in ethnically diverse papillon-Lefèvre syndrome patients. J. Med. Genet., 37, 927-932.
23. Holland, J.H. (1975) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Oxford, England.
24. Hu, J. and Yan, C. (2008) Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information. BMC Bioinformatics, 9, 297.
25. Huang, T. et al. (2010) Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties. PLoS One, 5, e11900.
26. Kittler, J. et al. (1998) On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell., 20, 226-239.
27. Krogh, A. et al. (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol., 305, 567-580.
28. Kubat, M. and Matwin, S. (1997) Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the 14th International Conference on Machine Learning. pp. 179-186.
29. Kwon, Y.K. and Moon, B.R. (2007) A hybrid neurogenetic approach for stock forecasting. IEEE Trans. Neural Netw., 18, 851-864.
30. Lenth, R. (2001) Some proctical guidelines for effective sample size determination. Am. Stat. J., 55, 187-193.
31. Lalle, P. et al. (2002) The founding members of xeroderma pigmentosum group G produce XPG protein with severely impaired endonuclease activity. J. Invest. Dermatol., 118, 344-351.
32. Li, B. et al. (2009) Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics, 25, 2744-2750.
33. Manolio, T.A. et al. (2009) Finding the missing heritability of complex diseases. Nature, 461, 747-753.
34. Monnier, N. (2001) Familial and sporadic forms of central core disease are associated with mutations in the C-terminal domain of the skeletal muscle ryanodine receptor. Hum. Mol. Genet., 10, 2581-2592.
35. Nakano, A. et al. (2001) Epidermolysis bullosa with congenital pyloric atresia: novel mutations in the beta 4 integrin gene (ITGB4) and genotype/phenotype correlations. Pediatric Res., 49, 618-626.
36. Ng, P.C. and Henikoff, S. (2001) Predicting deleterious amino acid substitutions. Genome Res., 11, 863-874.
37. Ng, P.C. and Henikoff, S. (2002) Accounting for human polymorphisms predicted to affect protein function. Genome Res., 12, 436-446. doi:10.1101/gr.212802.
38. Petersen, B. et al. (2009) A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct. Biol., 9, 51.
39. Petersen, T.N. et al. (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat. Methods, 8, 785-786.
40. Quevillon, E. et al. (2005) InterProScan: protein domains identifier. Nucleic Acids Res., 33, W116-W120.
41. Reva, B. et al. (2011) Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res., 39, e118. doi:10.1093/nar/gkr407.
42. Rokach, L. (2009) Ensemble-based classifiers. Artif. Intell. Rev., 33, 1-39.
43. Punta, M. et al. (2012) The Pfam protein families database. Nucleic Acids Res., 40, D290-D301.
44. Saeys, Y. et al. (2007) A review of feature selection techniques in bioinformatics. Bioinformatics, 23, 2507-2517.
45. Schaaf, A. et al. (2012) Multivariate modeling of complications with data driven variable selection: guarding against overfitting and effects of data set size. Radiother. Oncol., 105, 115-121.
46. Sherry, S.T. et al. (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res., 29, 308-311.
47. Simonaro, C.M. and Schuchman, E.H. (1995) N-acetylgalactosamine-4- sulfatase: identification of four new mutations within the conserved sulfatase region causing mucopolysaccharidosis type VI. Biochim. Biophys. Acta, 1272, 129-132.
48. Sistermans, E.A. et al. (2000) Mutation detection in the aspartoacylase gene in 17 patients with Canavan disease: four new mutations in the non-Jewish population. Eur. J. Hum. Genet., 8, 557-560.
49. Thomas, P.D. et al. (2003) PANTHER: a library of protein families and subfamilies indexed by function. Genome Res., 13, 2129-2141.
50. Thomas, P.D. et al. (2006) Applications for protein sequence-function evolution data: mRNA/protein expression analysis and coding SNP scoring tools. Nucleic Acids Res., 34, W645-W650.
51. Thusberg, J. et al. (2011) Performance of mutation pathogenicity prediction methods on missense variants. Hum. Mut., 32, 358-368.
52. Troyanskaya, O. et al. (2001) Missing value estimation methods for DNA microarrays. Bioinformatics, 17, 520-525.
53. Valentini, G. et al. (2002) Structure and function of human erythrocyte pyruvate kinase. Molecular basis of nonspherocytic hemolytic anemia. J. Biol. Chem., 277, 23807-23814.
54. Wei, Q. and Dunbrack, R.L. (2013) The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics. PLoS One, 8, e67863.
55. Yan, C. et al. (2006) Predicting DNA-binding sites of proteins from amino acid sequence. BMC Bioinformatics, 7, 262.
56. Yip, Y.L. et al. (2008) Annotating single amino acid polymorphisms in the UniProt/ Swiss-Prot knowledgebase. Hum. Mut., 29, 361-366.
57. Zhang, R. and Lin, Y. (2009) DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res., 37, D455-D458.