Machine learning methodology in bioinformatics

Springer Handbook of Bio-/Neuroinformatics

Volumn , Issue , 2014, Pages 185-206

  Campbell, Colin ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; BIOINFORMATICS; CLUSTER ANALYSIS; DISEASES; HIERARCHICAL SYSTEMS; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES;

BAYESIAN APPROACHES; BIOMEDICAL SCIENCE; CONFIDENCE MEASURE; DISEASE PROGRESSION; HIERARCHICAL CLUSTER ANALYSIS; KERNEL-BASED LEARNING; SUPERVISED AND UNSUPERVISED LEARNING; SUPPORT VECTOR MACHINE (SVMS);

UNSUPERVISED LEARNING;

EID: 85009072850     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-642-30574-0_12     Document Type: Chapter

References (69)

1. L. Bottou, O. Chapelle, D. DeCoste, J. Weston: Large-Scale Kernel Machines, Neural Information Processing Series (MIT Press, Cambridge 2007)
2. J. Platt, N. Cristianini, J. Shawe-Taylor: Large margin DAGS for multiclass classification, Adv. Neural Inform. Proces. Syst. 12, 547-553 (2000)
3. Y. Lee, Y. Lin, G. Wahba: Multicategory support vector machines, Technical Report 1043 (Univ. Madison, Wisconsin 2001)
4. T. Hastie, R. Tibshirani: Classification by pairwise coupling, Ann. Stat. 26, 451-471 (1998)
5. T.G. Dietterich, G. Bakiri: Solving multiclass learning problems via error-correcting output codes, J. Artif. Intell. 2, 263-286 (1995)
6. E.L. Allwein, R.E. Schapire, Y. Singer: Reducing multiclass to binary: A unifying approach for margin classifiers, J. Mach. Learn. Res. 1, 133-141 (2000)
7. K.-B. Duan, S.S. Keerthi: Which is the best multiclass SVM Method? An empirical study, Proc. 6th Int. Workshop Multiple Classifier Syst. (2005), Vol. 3541 (Springer, Berlin, Heidelberg 2006) pp. 278-285
8. C. Cortes, V. Vapnik: Support vector networks, Mach. Learn. 20, 273-297 (1995)
9. K. Veropoulos, C. Campbell, N. Cristianini: Controlling the sensitivity of support vector machines, Proc. Int. Joint Conf. Artif. Intell. (IJCAI) (1999)
10. J. Platt: Probabilistic outputs for support vector machines and comparison to regularised likelihood methods, Adv. Large Margin Classifiers (MIT Press, Cambridge 1999) pp. 61-74
11. A.E. Hoerl, R. Kennard: Ridge regression: Biased estimation for nonorthogonal problems, Technometrics 12, 55-67 (1970)
12. C. Saunders, A. Gammermann, V. Vovk: Ridge regression learning algorithm in dual variables, Proc. Fifteenth Int. Conf. Mach. Learn. (ICML), ed. by J. Shavlik (Morgan Kaufmann, 1998)
13. V. Vapnik: The Nature of Statistical Learning Theory (Springer, New York 1995)
14. V. Vapnik: Statistical Learning Theory (Wiley, New York 1998)
15. B. Schölkopf, A.J. Smola: Learning with Kernels (MIT Press, Cambridge 2002)
16. J. Weston, A. Gammerman, M. Stitson, V. Vapnik, V. Vovk, C. Watkins: Support vector density estimation, Advances in Kernel Methods: Support Vector Machines (MIT Press, Cambridge 1998) pp. 293-306
17. A.J. Smola, B. Schölkopf: A tutorial on support vector regression, Stat. Comput. 14, 199-222 (2004)
18. R.D. Williams, S.N. Hing, B.T. Greer, C.C. Whiteford, J.S. Wei, R. Natrajan, A. Kelsey, S. Rogers, C. Campbell, K. Pritchard-Jones, J. Khan: Prognostic classification of relapsing favourable histology Wilms tumour using cDNA microarray expression profiling and support vector machines, Genes Chromosom. Cancer 41, 65-79 (2004)
19. I. Guyon, A. Elisseeff: An Introduction to Variable and Feature Selection, J. Mach. Learn. Res. 3, 1157-1182 (2003)
20. T. Graepel, R. Herbrich, P. Bollmann-Sdorra, K. Obermayer: Classification on pairwise proximity data, Adv. Neural Inform. Proces. Syst. 11, 438-444 (1998)
21. E. Pekalska, P. Paclik, R.P.W. Duin: A generalized kernel approach to dissimilarity based classification, J. Mach. Learn. Res. 2, 175-211 (2002)
22. V. Roth, J. Laub, M. Kawanabe, J.M. Buhmann: Optimal cluster preserving embedding of nonmetric proximity data, IEEE Trans. Pattern Analys. Mach. Intell. 25, 1540-1551 (2003)
23. R. Luss, A. d’Aspremont: Support vector machine classification with indefinite kernels, Adv. Neural Inform. Proces. Syst. 20, 953-960 (2008)
24. Y. Ying, C. Campbell, M. Girolami: Analysis of SVM with Indefinite Kernels, Adv. Neural Informat. Proces. Syst. 22, 2205-2213 (2009)
25. N. Cristianini, C. Campbell, J. Shawe-Taylor: Dynamically adapting kernels in support vector machines, Adv. Neural Inform. Proces. Syst. 11, 204-210 (1999)
26. T. Joachims: Estimating the generalization performance of an SVM efficiently, Proc. 17th Int. Conf. Mach. Learn. (Morgan Kaufmann, 2000) pp. 431-438
27. O. Chapelle, V. Vapnik: Model selection for support vector machines, Adv. Neural Inform. Proces. Syst. 12, 673-680 (2000)
28. V. Vapnik, O. Chapelle: Bounds on error expectation for support vector machines, Neural Comput. 12, 2013-2036 (2000)
29. P. Sollich: Bayesian methods for support vector machines: Evidence and predictive class probabilities, Mach. Learn. 46, 21-52 (2002)
30. J. Shawe-Taylor, N. Cristianini: Kernel Methods for Pattern Analysis (Cambridge Univ. Press, Cambridge 2004)
31. H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, C. Watkins: Text classification using string kernels, J. Mach. Learn. Res. 2, 419-444 (2002)
32. C. Leslie, R. Kuang: Fast kernels for inexact string matching, 16th Ann. Conf. Learning Theory 7th Kernel Workshop, Vol. 2777 (Springer, Berlin, Heidelberg 2003) pp. 114-128
33. S. Vishwanathan, A. Smola: Fast Kernels for String and Tree Matching, Adv. Neural Inform. Proces. Syst. 15, 569-576 (2003)
34. I.R. Kondor, J.D. Lafferty: Diffusion kernels on graphs and other discrete structures, Proc. Int. Conf. Mach. Learn. (Morgan Kaufmann, San Francisco, 2002) pp. 315-322
35. A.J. Smola, I.R. Kondor: Kernels and regularization on graphs, Conf. Learning Theory (COLT), Vol. 2777 (Springer, Berlin, Heidelberg 2003) pp. 144-158
36. T. Gartner, P. Flach, S. Wrobel: On graph kernels: Hardness results and efficient alternatives, Proc. Annu. Conf. Computational Learning Theory (COLT) (Springer, Berlin, Heidelberg 2003) pp. 129-143
37. S.V.N. Vishwanathan, K.M. Borgwardt, I.R. Kondor, N.N. Schraudolph: Graph Kernels, J. Mach. Learn. Res. 9, 1-41 (2008)
38. G.R.G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, M.I. Jordan: Learning the kernel matrix with semidefinite programming, J. Mach. Learn. Res. 5, 27-72 (2004)
39. F. Bach, G.R.G. Lanckriet, M.I. Jordan: Multiple kernel learning, conic duality and the SMO algorithm, Proc. 21st Int. Conf. Machine Learning (ICML) (Morgan Kaufmann, New York 1998)
40. S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf: Large scale multiple kernel learning, J. Mach. Learn. Res. 7, 1531-1565 (2006)
41. A. Rakotomamonjy, F. Bach, S. Canu, Y. Grandvalet: SimpleMKL, J. Mach. Learn. Res. 9, 2491-2521 (2008)
42. Z. Xu, R. Jin, I. King, M.R. Lyu: An extended level method for multiple kernel learning, Adv. Neural Inform. Proces. Syst. 22, 1825-1832 (2008)
43. Y. Ying, K. Huang, C. Campbell: Enhanced protein fold recognition through a novel data integration approach, BMC Bioinf. 10, 267-285 (2009)
44. T. Damoulas, M. Girolami: Probabilistic multi-class multi-kernel learning: On protein fold recognition and remote homology detection, Bioinformatics 24, 1264-1270 (2008)
45. G.R.G. Lanckriet, T. De Bie, N. Cristianini, M.I. Jordan, W.S. Noble: A statistical framework for genomic data fusion, Bioinformatics 20, 2626-2635 (2004)
46. M. Kloft, U. Brefeld, S. Sonnenburg, P. Laskov, K.-R. Müller, A. Zien: Efficient and accurate lp-norm multiple kernel learning, Adv. Neural Inform. Proces. Syst. 22, 997-1005 (2009)
47. U. Alon, N. Barkai, D.A. Notterman, K. Gish, S. Ybarra, D. Mack, A.J. Levine: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays, Proc. Natl. Acad. Sci. USA 96(12), 6745-6750 (1999)
48. B. Everitt: Cluster Analysis (Arnold, New York 1993)
49. L. Kaufman, P.J. Rousseeuw: Finding Groups in Data (Wiley, New York 2005)
50. R.O. Duda, P.E. Hart, D.G. Stork: Pattern Classification (Wiley, New York 2001)
51. Y.W. Teh, D. Newman, M. Welling: A collapsed variational Bayesian inference algorithm for latent dirichlet allocation, Adv. Neural Inform. Proces. Syst. 19, 1353-1360 (2006)
52. Y. Ying, P. Li, C. Campbell: A marginalized variational Bayesian approach to the analysis of array data, BMC Proc. 2(4), S7 (2008)
53. P. Li, Y. Ying, C. Campbell: A variational approach to semi-supervised clustering, Proc. ESANN2009 (2009) pp. 11-16
54. D.M. Blei, M.I. Jordan: Modeling annotated data, Proc. 26th Annu. Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. (ACM Press, New York 2003) pp. 127-134
55. P. Agius, Y. Ying, C. Campbell: Bayesian Unsupervised Learning with Multiple Data Types, Stat. Appl. Genet. Molec. Biol. 8, 27 (2009)
56. S. Rogers, M. Girolami, C. Campbell, R. Breitling: The latent process decomposition of cdna microarray datasets, IEEE/ACM Trans. Comput. Biol. Bioinforma. 2, 143-156 (2005)
57. C. Blenkiron, L.D. Goldstein, N.P. Thorne, I. Spiteri, S.F. Chin, M.J. Dunning, N.L. Barbosa-Morais, A.E. Teschendorff, A.R. Green, I.O. Ellis, S. Tavaré, C. Caldas, E.A. Miska: MicroRNA expression profiling of human breast cancer identifies new markers of tumour subtype, Genome Biol. 8(10), R214-1-R214-16 (2007)
58. L. Carrivick, S. Rogers, J. Clark, C. Campbell, M. Girolami, C. Cooper: Identification of prognostic signatures in breast cancer microarray data using Bayesian techniques, J. R. Soc. Interf. 3, 367-381 (2006)
59. E. Garber, O.G. Troyanskaya, K. Schluens, S. Petersen, Z. Thaesler, M. Pacyna-Gengelbach, M. van de Rijn, G.D. Rosen, C.M. Perou, R.I. Whyte, R.B. Altman, P.O. Brown, D. Botstein, I. Petersen: Diversity of gene expression in adenocarcinoma of the lung, Proc. Natl. Acad. Sci. USA 98, 13784-13789 (2001)
60. C. Andrieu, N. De Freitas, A. Doucet, M.I. Jordan: An introduction to MCMC for machine learning, Mach. Learn. 50, 5-43 (2003)
61. W.R. Gilks, S. Richardson, D.J. Spiegelhalter: Markov Chain Monte Carlo in Practice (Chapman Hall/CRC, New York 1996)
62. C.P. Robert, G. Casella: Monte Carlo Statistical Methods (Springer, Berlin, Heidelberg 2004)
63. S. Chib, E. Greenberg: Understanding the Metropolis Hastings Algorithm, Am. Stat. 49(4), 327-335 (1995)
64. B.A. Berg: Markov Chain Monte Carlo Simulations and Their Statistical Analysis (World Scientific, Singapore 2004)
65. W.M. Bolstad: Understanding Computational Bayesian Statistics (Wiley, New York 2010)
66. K. Bleakley, G. Biau, J.-P. Vert: Supervised reconstruction of biological networks with local models, Bioinformatics 23, i57-i65 (2007)
67. B. Calderhead, M. Girolami: Estimating Bayes factors via thermodynamic integration and population MCMC, Comput. Stat. Data Anal. 53, 4028-4045 (2009)
68. T.R. Xu, V. Vyshemirsky, A. Gormand, A. von Kriegsheim, M. Girolami, G.S. Baillie, D. Ketley, A.J. Dunlop, G. Milligan, M.D. Houslay, W. Kolch: Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species, Sci. Signal. 3(113), ra20: 1-ra20: 10 (2010)
69. Cancer Genome Atlas: Available at http://cancergenome.nih.gov