Regularized least squares classifiers may predict Crohn's disease from profiles of single nucleotide polymorphisms

Annals of Human Genetics

Volumn 71, Issue 4, 2007, Pages 537-549

  D'Addabbo, A ^a   Latiano, A ^b   Palmieri, O ^b   Maglietta, R ^a   Annese, V ^b   Ancona, N ^a  

^a INSTITUTE OF INTELLIGENT SYSTEMS FOR AUTOMATION   (Italy)

^b CASA SOLLIEVO DELLA SOFFERENZA HOSPITAL   (Italy)

Author keywords

CARD15; Crohn's disease; Prediction; Single nucleotide polymorphisms

Indexed keywords

CASPASE RECRUITMENT DOMAIN PROTEIN 15;

ADOLESCENT; ADULT; AGED; ARTICLE; CASE CONTROL STUDY; CHILD; CONTROLLED STUDY; CROHN DISEASE; DIAGNOSTIC ACCURACY; FEMALE; GENE INTERACTION; GENETIC CORRELATION; GENETIC MARKER; GENETIC PREDISPOSITION; GENETIC SUSCEPTIBILITY; GENETIC VARIABILITY; HUMAN; MAJOR CLINICAL STUDY; MALE; METHODOLOGY; MICROARRAY ANALYSIS; PHENOTYPE; POPULATION GENETICS; PREDICTION; PRIORITY JOURNAL; REGRESSION ANALYSIS; SAMPLE SIZE; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE;

ALGORITHMS; CLUSTER ANALYSIS; CROHN DISEASE; GENE FREQUENCY; HUMANS; LEAST-SQUARES ANALYSIS; MODELS, GENETIC; MODELS, STATISTICAL; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 34249873030     PISSN: 00034800     EISSN: 14691809     Source Type: Journal    
DOI: 10.1111/j.1469-1809.2007.00349.x     Document Type: Article

References (44)

1. Ambroise, C. & McLachlan, G. (2002) Selection bias in gene extraction on the basis of microarray gene-expression data, Proc Natl Acad Sci 99, 6562-6566.
2. Ancona, N., Cicirelli, G., Stella, E. & Distante, A. (2003) Ball detection in static images with support vector machines for classication, Image and Vision Computing 21, 675-692.
3. Ancona, N., Maglietta, R., D'Addabbo, A., Liuni, S. & Pesole, G. (2005) Regularized least squares cancer classifiers from dna microarray data, BMC-Bioinformatics 6, (Suppl 4), S2.
4. Aronszajn, N. (1950) Theory of reproducing kernels, Transactions of the American Math-ematical Society 68 (3), 337-404.
5. Bishop, C. (1995) Neural Networks for Pattern Recognition, Oxford University Press.
6. Brown, M. P. S., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T. S., Jr., M. A. & Haussler, D. (2000) Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc Natl Acad Sci 97, 262-267.
7. Distante, C., Ancona, N. & Siciliano, P. (2003) Support vector machines for olfactory signals recognition, Sensors and Actuators B 88, 30-39.
8. Economou, M., Trikalinos, T., Loizou, K., Tsianos, E. & Ioannidis, J. (2004) Differential effects of nod2 variants on crohn's disease risk and phenotype in diverse populations: A metaanalysis, Am J Gastroenterol 99 (12), 2393-404.
9. Frankel, W. & Schork, N. (1996) Who's afraid of epistatis?, Nat Genet 14, 371-3.
10. Gaya, D., Russll, R., Nimmo, E. & Satsangi, J. (2006) New genes in inammatory bowel disease: Lesson from complex disease, Lancet 367, 1271-84.
11. Girosi, F. (1998) An Equivalence Between Sparse Approximation And Support Vector Machines, Neural Comp 10 (6), 1455-1480.
12. Girosi, F., Jones, M. & Poggio, T. (1995) Regularization theory and neural networks architectures, Neural Computation 7, 219-269.
13. Golland, P., Liang, F., Mukherjee, S. & Panchenko, D. (2005) Permutation tests for classication, in P. Auer & R. Meir, eds, 'Lecture Notes in Computer Science, Volume 3559, Springer Berlin/ Heidelberg, pp. 501-515.
14. Good, P. (1994) Permutation tests: A practical guide to resampling methods for testing hypothesis, Springer Verlag.
15. Heisele, B., Poggio, T. & Pontil, M. (2000) Face detection in still gray images, Technical Report A.I. Memo No. 1687, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
16. Latiano, A., Palmieri, O., Valvano, R., D'Inca, R., Vecchi, M., Ferraris, A. & et al. (2006) Contribution of ibd5 locus to clinical features of ibd patients, Am J Gastroenterol 101, 318-25.
17. Listgarten, J., Damaraju, S., Poulin, B., Cook, L., Dufour, J., Driga, A., Mackey, J., Wishart, D., Greiner, R. & Zanke, B. (2004) Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms, Clinical Cancer Re-search 10, 2725-2737.
18. Luntz, A. & Brailovsky, V. (1969) On estimation of characters obtained in statistical procedure of recognition, Technicheskaya Kibernetica 3.
19. Maglietta, R., D'Addabbo, A., Piepoli, A., Perri, F., Liuni, S., Pesole, G. & Ancona, N. (2007), 'Selection of relevant genes in cancer diagnosis based on their prediction accuracy, Artificial Intelligence in Medicine. in press.
20. Mukherjee, S., Tamayo, P., Mesirov, J., Slonim, D., Verri, A. & Poggio, T. (1999) Support vector machine classification of microarray data, Technical Report CBCL Paper #182/AI Memo #1677, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
21. Mukherjee, S., Tamayo, P., Rogers, S., Rifkin, R., Engle, A., Campbell, C., Golub, T. & Mesirov, J. (2003) Estimating dataset size requirements for classifying dna microarray data, J Comp Biol 10, 119-142.
22. Nichols, T. & Holmes, A. (2001) Nonparametric permutation tests for functional neuroimaging: A primer with examples, Hum Brain Mapp 15, 1-25.
23. Palmieri, O., Latiano, A., Valvano, R., D'Inca, R., Vecchi, M., Sturniolo, G. & et al. (2005) Multidrug resistance 1 gene polymorphisms are not associated with inflammatory bowel disease and response to therapy in italian patients, Aliment Pharmacol Ther 22, 1129-38.
24. Papoulis, A. (1962) The Fourier integral and its applications, McGraw-Hill Book Company.
25. Penco, S., Grossi, E., Cheng, S., Intraligi, M., Maurelli, G., Patrosso, M. C., Marocchi, A. & Buscema, M. (2005) Assessment of the role of genetic polymorphism in venous thrombosis through artificial neural networks, Ann Hum Genet 69, 693-706.
26. Podolsky, D. (2002) Inammatory bowel disease, N Engl J Med 347 (6), 417-29.
27. Poggio, T. & Girosi, F. (1989) A theory of networks for approximation and learning, Technical Report A.I. Memo No. 1140, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
28. Poggio, T. & Girosi, F. (1990) Networks for approximation and learning, Proceedings of the IEEE 78 (9), 1481-1497.
29. Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J., Poggio, T., Gerald, W., Loda, M., Lander, E. & Golub, T. (2001) Multiclass cancer diagnosis using tumor gene expression signatures, Proc Natl Acad Sci 98, 15149-15154.
30. Rifkin, R., Yeo, G. & Poggio, T. (2003) Regularized least squares classification, Advances in Learning Theory: Methods, Model and Applications, NATO Science Series III: Computer and Systems Sciences, Edited by Suykens, Horvath, Basu, Micchelli, and Vandewalle, Amsterdam: IOS Press 190, 131-153.
31. Risch, N. J. (2000) Searcing for genetic determinants in the new millennium, Nature 405 (6788), 847-856.
32. Sambrook, J., Fritsch, E. & Maniatis, F. (1989) Molecular Cloning: A Laboratory Manual, Cold Spring Harbor, New York: Cold Spring Harbor Laboratory Press.
33. Schoelkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T. & Vapnik, V. (1997) Comparing support vector machines with gaussian kernels to radial basis function classifiers, IEEE Transactions on Signal Processing 45 (11), 2758-2765.
34. Schwender, H., Zucknick, M., Ickstadt, K. & Bolt, H. (2004) A pilot study on the application of statistical classification procedures to molecular epidemiological data, Toxicology Letters 151 (1), 291-299.
35. Shanahan, F. (2002) Crohn's disease, Lancet 359, 62-9.
36. Sillanpää, M. & Auranen, K. (2004) Replication in genetic studies of complex traits, Ann Human Genet 68, 646-57.
37. Tikhonov, A. & Arsenin, V. (1977) Solutions of ill-posed problems, W. H. Winston, Washington D.C.
38. Tomida, S., Hanai, T., Koma, N., Suzuki, Y., Kobayashi, T. & Honda, H. (2002) Articial neural network predictive model for allergic disease using single nucleotide polymorphisms data, Journal of Bioscience and Bioengineering 93 (5), 470-478.
39. Tomita, Y., Tomida, S., Hasegawa, Y., Suzuki, Y., Shirakawa, T., Kobayashi, T. & Honda, H. (2004) Articial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma, BMC Bioinformatics 5 (1), 120.
40. Vapnik, V. (1995) The Nature of Statistical Learning Theory, Springer Verlag.
41. Vapnik, V. (1998) Statistical Learning Theory, John Wiley & Sons, INC.
42. Wahba, G. (1990) Spline models for observational data, CBMS-NSF Regional Conference Series in Applied Mathematics, volume 59, Society for Industrial & Applied Mathematics.
43. Yoon, Y., Song, J., Hong, S. & Kim, J. (2003) Analysis of multiple single nucleotide polymorphisms of candidate genes related to coronary heart disease susceptibility by using support vector machines, Clin Chem Lab Med 41 (4), 529-534.
44. Zhang, P. & Peng, J. (2004) Svm vs regularized least squares classification, in 'Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04): 23-26 Aug. 2004', IEEE Computer Society, pp. 176-179.