Kernel methods in genomics and computational biology

Kernel Methods in Bioengineering, Signal and Image Processing

Volumn , Issue , 2006, Pages 42-63

  Vert, Jean Philippe ^a  

^a PSL RESEARCH UNIVERSITY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84900616454     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59904-042-4.ch002     Document Type: Chapter

References (106)

1. Aebersold, R., & Mann, M. (2003). Mass spectrometry-based proteomics. Nature, 422(6928), 198-207.
2. Aliferis, C., Hardin, D., & Massion, P. (2002). Machine learning models for lung cancer classification using array comparative genomic hybridization. In Proceedings of the 2002 American Medical Informatics Association (AMIA) Annual Symposium (pp. 7-11).
3. Ambroise, C., & McLachlan, G. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. In Proceedings of the National Academy of Science USA, 99(10), 6562-6566.
4. Bao, L., & Sun, Z. (2002). Identifying genes related to drug anticancer mechanisms using support vector machine. FEBS Letters, 521, 109-114.
5. Ben-Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M., & Yakhini, Z. (2000). Tissue classification with gene expression profiles. Journal of Computational Biology, 7(3-4), 559-583.
6. Ben-Hur, A., & Brutlag, D. (2003). Remote homology detection: A motif based approach. Bioinformatics, 19(Suppl. 1), i26-i33.
7. Ben-Hur, A., & Noble, W. S. (2005). Kernel methods for predicting protein-protein interactions. Bioinformatics, 21(1), i38-i46.
8. Bhasin, M., & Raghava, G. P. S. (2004). Prediction of CTL epitopes using QM, SVM, and ANN techniques. Vaccine, 22(23-24), 3195-3204.
9. Bock, J. R., & Gough, D. A. (2001). Predicting protein-protein interactions from primary structure. Bioinformatics, 17(5), 455-460.
10. Bock, J. R., & Gough, D. A. (2002). A new method to estimate ligand-receptor energetics. Mol Cell Proteomics, 1(11), 904-910.
11. Borgwardt, K.M., Ong, C.S., Schönauer, S., Vishwanathan, S.V.N., Smola, A.J., Kriegel, H.-P. (2005). Protein function prediction via graph kernels. Bioinformatics, 21(1), i47-i56.
12. Cai, C., Wang, W., Sun, L., & Chen, Y. (2003). Protein function classification via support vector machine approach. Mathematical Biosciences, 185(2), 111-122.
13. Camps-Valls, G., Chalk, A., Serrano-Lopez, A., Martin-Guerrero, J., & Sonnhammer, E. (2004). Profiled support vector machines for antisense oligonucleotide efficacy prediction. BMC Bioinformatics, 5(135), 135.
14. Chen, Y., Lin, Y., Lin, C., & Hwang, J. (2004). Prediction of the bonding states of cysteines using the support vector machines based on multiple feature vectors and cysteine state sequences. Proteins, 55(4), 1036-1042.
15. Cuturi, M., & Vert, J.-P. (2005). The context-tree kernel for strings. Neural Networks, 18(4), 1111-1123.
16. Degroeve, S., Saeys, Y., De Baets, B., Rouze, P., & Van de Peer, Y. (2005). SpliceMachine: Predicting splice sites from high-dimensional local context representations. Bioinformatics, 21, 1332-1338.
17. DeRisi, J. L., Iyer, V. R., & Brown, P. O. (1997). Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 278(5338), 680-686.
18. Ding, C., & Dubchak, I. (2001). Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 17, 349-358.
19. Dobson, P., & Doig, A. (2003). Distinguishing enzyme structures from non-enzymes without alignments. Journal of Molecular Biology, 330(4), 771-783.
20. Dönnes, P., Elofsson, A. (2002). Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics, 3(1), 25.
21. Dror, G., Sorek, R., & Shamir, R. (2005). Accurate identification of alternatively spliced exons using support vector machine. Bioinformatics, 21(7), 897-901.
22. Durbin, R., Eddy, S., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press.
23. Friedel, C. C., Jahn, K. H. V., Sommer, S., Rudd, S., Mewes, H. W., & Tetko, I. V. (2005). Support vector machines for separation of mixed plant-pathogen EST collections based on codon usage. Bioinformatics, 21, 1383-1388.
24. Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., & Haussler, D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16(10), 906-914.
25. Guermeur, Y., Lifschitz, A., & Vert, R. (2004). A kernel for protein secondary structure prediction. In B. Schölkopf, K. Tsuda, & J. Vert (Eds.), Kernel methods in computational biology(pp. 193-206). MIT Press.
26. Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1-3), 389-422.
27. Haussler, D. (1999). Convolution kernels on discrete structures (Tech. Rep. No. UCSCCRL-99-10). Santa Cruz: University of California.
28. Hua, S., & Sun, Z. (2001a). A novel method of protein secondary structure prediction with high segment overlap measure: Support vector machine approach. Journal of Molecular Biology, 308(2), 397-407.
29. Hua, S., & Sun, Z. (2001b). Support vector machine approach for protein subcellular localization prediction. Bioinformatics, 17(8), 721-728.
30. Huang, T. M., & Kecman, V. (2005). Gene extraction for cancer diagnosis by support vector machines: An improvement. Artificial Intelligence in Medicine (Special Issue on Computational Intelligence Techniques in Bioinformatics), 35, 185-194.
31. International Human Genome Sequencing Consortium. (2001). Initial sequencing and analysis of the human genome. Nature, 409(6822), 860-921.
32. Jaakkola, T., Diekhans, M., & Haussler, D. (2000). A discriminative framework for detecting remote protein homologies. Journal of Computational Biology, 7(1-2), 95-114.
33. Jaakkola, T. S., Diekhans, M., & Haussler, D. (1999). Using the Fisher kernel method to detect remote protein homologies. In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, 149-158.
34. Karchin, R., Karplus, K., & Haussler, D. (2002). Classifying G-protein coupled receptors with support vector machines. Bioinformatics, 18, 147-159.
35. Karklin, Y., Meraz, R. F., & Holbrook, S. R. (2005). Classification of non-coding RNA using graph representations of secondary structure. In Pacific Symposium on Biocomputing (pp. 4-15).
36. Kashima, H., Tsuda, K., & Inokuchi, A.(2004). Kernels for graphs. In B. Schölkopf, K. Tsuda, & J. Vert (Eds.),Kernel methods in computational biology (pp. 155-170). MIT Press.
37. Kim, J. H., Lee, J., Oh, B., Kimm, K., & Koh, I. (2004). Prediction of phosphorylation sites using SVMs. Bioinformatics, 20(17), 3179-3184.
38. Kin, T., Tsuda, K., & Asai, K. (2002). Marginalized kernels for RNA sequence data analysis. In R. Lathtop, K. Nakai, S. Miyano, T. Takagi, & M. Kanehisa (Eds.), Genome informatics 2002 (pp. 112-122). Universal Academic Press.
39. Komura, D., Nakamura, H., Tsutsumi, S., Aburatani, H., & Ihara, S. (2005). Multidimensional support vector machines for visualization of gene expression data. Bioinformatics, 21(4), 439-444.
40. Kondor, R., & Vert, J.-P. (2004). Diffusion kernels. In B. Schölkopf, K. Tsuda, & J. Vert, (Eds.), Kernel methods in computational biology (pp. 171-192). MIT Press.
41. Krishnapuram, B., Carin, L., & Hartemink, A. (2004). Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data. Journal of Computational Biology, 11(2-3), 227-242.
42. Lanckriet, G. R. G., De Bie, T., Cristianini, N., Jordan, M. I., & Noble, W. S. (2004). A statistical framework for genomic data fusion. Bioinformatics, 20(16), 2626-2635.
43. Leslie, C., Eskin, E., & Noble, W. (2002). The spectrum kernel: A string kernel for SVM protein classification. In Proceedings of the Pacific Symposium on Biocomputing 2002 (pp. 564-575).
44. Leslie, C. S., Eskin, E., Cohen, A., Weston, J., & Noble, W. S. (2004). Mismatch string kernels for discriminative protein classification. Bioinformatics, 20(4), 467-476.
45. Li, T., Zhang, C., & Ogihara, M. (2004). A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics, 20(15), 2429-2437.
46. Liao, L., & Noble, W. (2003). Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships. Journal of Computational Biology, 10(6), 857-868.
47. Lin, K., Kuang, Y., Joseph, J. S., & Kolatkar, P. R. (2002). Conserved codon composition of ribosomal protein coding genes in Escherichia coli, mycobacterium tuberculosis and saccharomyces cerevisiae: Lessons from supervised machine learning in functional genomics. Nucleic Acids Research, 30(11), 2599-2607.
48. Listgarten, J., Damaraju, S., Poulin, B., Cook, L., Dufour, J., Driga, A., et al. (2004). Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms. Clinical Cancer Research, 10(8), 2725-2737.
49. Logan, B., Moreno, P., Suzek, B., Weng, Z., & Kasif, S. (2001). A study of remote homology detection (Tech. Rep. No. CRL 2001/05). Compaq Cambridge Research laboratory.
50. Mahé, P., Ueda, N., Akutsu, T., Perret, J.-L., & Vert, J.-P. (2005). Graph kernels for molecular structure-activity relationship analysis with support vector machines. Journal of Chemical Information and Modeling, 45(4), 939-951.
51. Martin, S., Roe, D., & Faulon, J.-L. (2005). Predicting protein-protein interactions using signature products. Bioinformatics, 21(2), 218-226.
52. Matsuda, A., Vert, J.-P., Saigo, H., Ueda, N., Toh, H., & Akutsu, T. (2005). A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Science, 14(11), 2804-2813.
53. Meinicke, P., Tech, M., Morgenstern, B., & Merkl, R. (2004). Oligo kernels for datamining on biological sequences: A case study on prokaryotic translation initiation sites. BMC Bioinformatics, 5, 169.
54. Meireles, S., Carvalho, A., Hirata, R., Montagnini, A., Martins, W., Runza, F., et al. (2003). Differentially expressed genes in gastric tumors identified by cDNA array. Cancer Letters, 190(2), 199-211.
55. Middendorf, M., Ziv, E., Adams, C., Hom, J., Koytcheff, R., Levovitz, C., et al. (2004). Discriminative topological features reveal biological network mechanisms. BMC Bioinformatics, 5, 181.
56. Model, F., Adorjan, P., Olek, A., & Piepenbrock, C. (2001). Feature selection for DNA methylation based cancer classification. Bioinformatics, 17(1), S157-S164.
57. Moler, E. J., Chow, M. L., & Mian, I. S. (2000). Analysis of molecular profile data using generative and discriminative methods. Physiological Genomics, 4(2), 109-126.
58. Mukherjee, S., Tamayo, P., Mesirov, J. P., Slonim, D., Verri, A., & Poggio, T. (1998). Support vector machine classification of microarray data (Tech. Rep. No. 182).
59. O'Donnell, R. K., Kupferman, M., Wei, S.J., Singhal, S., Weber, R., O'Malley, B., et al. (2005). Gene expression signature predicts lymphatic metastasis in squamous cell carcinoma of the oral cavity. Oncogene, 24(7), 1244-1251.
60. O'Flanagan, R. A., Paillard, G., Lavery, R., Sengupta, A. M. (2005). Non-additivity in protein-DNA binding. Bioinformatics, 21(10), 2254-2263.
61. Park, K.-J., & Kanehisa, M. (2003). Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics, 19(13), 1656-1663.
62. Passerini, A., & Frasconi, P. (2004). Learning to discriminate between ligand-bound and disulfide-bound cysteines. Protein Engineering Design and Selection, 17(4), 367-373.
63. Pavlidis, P., Weston, J., Cai, J., & Noble, W. (2002). Learning gene functional classifications from multiple data types. Journal of Computational Biology, 9(2), 401-411.
64. Pochet, N., De Smet, F., Suykens, J. A. K., & De Moor, B. L. R. (2004). Systematic benchmarking of microarray data classification: Assessing the role of non-linearity and dimensionality reduction. Bioinformatics, 20(17), 3185-3195.
65. Qin, J., Lewis, D. P., & Noble, W. S. (2003). Kernel hierarchical gene clustering from microarray expression data. Bioinformatics, 19(16), 2097-2104.
66. Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C., Angelo, M., et al. (2001). Multiclass cancer diagnosis using tumor gene expression signatures. In Proceedings of the National Academy of Science USA, 98(26), 15149-15154.
67. Rätsch, G., Sonnenburg, S., & Schölkopf, B. (2005). RASE: Recognition of alternatively spliced exons in c. elegans. Bioinformatics, 21(Suppl. 1), i369-i377.
68. Res, I., Mihalek, I., & Lichtarge, O. (2005). An evolution based classifier for prediction of protein interfaces without using protein structures. Bioinformatics, 21(10), 24962501rx.
69. Rose, J. R., Turkett, W. H. J., Oroian, I. C., Laegreid, W. W., & Keele, J. (2005). Correlation of amino acid preference and mammalian viral genome type. Bioinformatics, 21(8), 1349-1357.
70. Saigo, H., Vert, J.-P., Ueda, N., & Akutsu, T. (2004). Protein homology detection using string alignment kernels. Bioinformatics, 20(11), 1682-1689.
71. Schena, M., Shalon, D., Davis, R., & Brown, P. (1995). Quantitative monitoring of gene expression patterns with a complimentary DNA microarray. Science, 270, 467-470.
72. Schölkopf, B., Tsuda, K., & Vert, J.-P. (2004). Kernel methods in computational biology. MIT Press.
73. Seeger, M. (2002). Covariance kernels from Bayesian generative models. Advances in Neural Information Processing Systems, 14, 905-912.
74. Segal, N. H., Pavlidis, P., Antonescu, C. R., Maki, R. G., Noble, W. S., DeSantis, D., et al. (2003). Classification and subtype prediction of adult soft tissue sarcoma by functional genomics. American Journal of Pathology, 163(2), 691-700.
75. Segal, N. H., Pavlidis, P., Noble, W. S., Antonescu, C. R., Viale, A., Wesley, U. V., et al. (2003). Classification of clear-cell sarcoma as a subtype of melanoma by genomic profiling. Journal of Clinical Oncology, 21(9), 1775-1781.
76. Sharan, R., & Myers, E. W. (2005). A motif-based framework for recognizing sequence families. Bioinformatics, 21(Suppl. 1), i387-i393.
77. Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge University Press.
78. Shipp, M. A., Ross, K. N., Tamayo, P., Weng, A. P., Kutok, J. L., Aguiar, R. C. T., et al. (2002). Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine, 8(1), 68-74.
79. Smith, T., & Waterman, M. (1981). Identification of common molecular subsequences. Journal of Molecular Biology, 147, 195-197.
80. Steiner, G., Suter, L., Boess, F., Gasser, R., de Vera, M. C., Albertini, S. et al. (2004). Discriminating different classes of toxicants by transcript profiling. Environmental Health Perspectives, 112(12), 1236-1248.
81. Su, Y., Murali, T., Pavlovic, V., Schaffer, M., & Kasif, S. (2003). RankGene: Identification of diagnostic genes based on expression data. Bioinformatics, 19(12), 1578-1579.
82. Swamidass, S. J., Chen, J., Bruand, J., Phung, P., Ralaivola, L., & Baldi, P. (2005). Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity. Bioinformatics, 21(Suppl. 1), i359-i368.
83. Teramoto, R., Aoki, M., Kimura, T., & Kanaoka, M. (2005). Prediction of siRNA functionality using generalized string kernel and support vector machine. FEBS Letters, 579(13), 2878-2882.
84. Tsuda, K., Kin, T., & Asai, K. (2002). Marginalized kernels for biological sequences. Bioinformatics, 18, S268-S275.
85. Tsuda, K., & Noble, W. (2004). Learning kernels from biological networks by maximizing entropy. Bioinformatics, 20, i326-i333.
86. Valentini, G. (2002). Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles. Artificial Intelligence in Medicine, 26(3), 281-304.
87. Venter, J.C.e a. (2001). The sequence of the human genome. Science, 291(5507), 1304-1351.
88. Vert, J.-P. (2002). A tree kernel to analyze phylogenetic profiles. Bioinformatics, 18, S276-S284.
89. Vert, J.-P., & Kanehisa, M. (2003). Extracting active pathways from gene expression data. Bioinformatics, 19, 238ii-234ii.
90. Vert, J.-P., Saigo, H., & Akutsu, T. (2004). Local alignment kernels for biological sequences. In B. Schölkopf, K. Tsuda, & J. Vert, (Eds.), Kernel methods in computational biology (pp. 131-154). Cambridge, MA: MIT Press.
91. Vert, J.-P., Thurman, R., & Noble, W. S. (2006). Kernels for gene regulatory regions. In Y. Weiss, B. Schölkopf, & J. Platt (Eds.), Advances in Neural Information Processing Systems 18 (pp. 1401-1408). Cambridge, MA: MIT Press.
92. Vert, J.-P., & Yamanishi, Y. (2005). Supervised graph inference. Advances in Neural Information Processing Systems, 17, 1433-1440.
93. Wagner, M., Naik, D., & Pothen, A. (2003). Protocols for disease classification from mass spectrometry data. Proteomics, 3(9), 1692-1698.
94. Wang, M., Yang, J., Liu, G.-P., Xu, Z.-J., & Chou, K.-C. (2004). Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition. Protein Engineering Design and Selection, 17(6), 509-516.
95. Watkins, C. (2000). Dynamic alignment kernels. In A. J. Smola, P. L. Bartlett, B. Schölkopf, & D. Schuurmans (Eds.), Advances in large margin classifiers (pp. 39-50). Cambridge, MA: MIT Press.
96. Williams, R., Hing, S., Greer, B., Whiteford, C., Wei, J., Natrajan, R., et al. (2004). Prognostic classification of relapsing favorable histology Wilms tumor using cDNA microarray expression profiling and support vector machines. Genes Chromosomes Cancer, 41(1), 65-79.
97. Wu, B., Abbott, T., Fishman, D., McMurray, W., Mor, G., Stone, K., et al. (2003). Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics, 19(13), 1636-1643.
98. Yamanishi, Y., Vert, J.-P., & Kanehisa, M. (2004). Protein network inference from multiple genomic data: A supervised approach. Bioinformatics, 20, i363-i370.
99. Yan, C., Dobbs, D., & Honavar, V. (2004). A two-stage classifier for identification of proteinprotein interface residues. Bioinformatics, 20(Suppl. 1), i371-i378.
100. 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. Clinical Chemistry and Laboratory Medicine, 41(4), 529-534.
101. Yu, C., Zavaljevski, N., Stevens, F. J., Yackovich, K., & Reifman, J. (2005). Classifying noisy protein sequence data: A case study of immunoglobulin light chains. Bioinformatics, 21(Suppl. 1), i495-i501.
102. Yuan, Z., Burrage, K., & Mattick, J. (2002). Prediction of protein solvent accessibility using support vector machines. Proteins, 48(3), 566-570.
103. Zavaljevski, N., Stevens, F., & Reifman, J. (2002). Support vector machines with selective kernel scaling for protein classification and identification of key amino acid positions. Bioinformatics, 18(5), 689-696.
104. Zhang, S.-W., Pan, Q., Zhang, H.-C., Zhang, Y.-L., & Wang, H.-Y. (2003). Classification of protein quaternary structure with support vector machine. Bioinformatics, 19(18), 2390-2396.
105. Zhang, X.H.-F., Heller, K. A., Hefter, I., Leslie, C. S., & Chasin, L. A. (2003). Sequence information for the splicing of human pre-mRNA identified by support vector machine classification. Genome Research, 13(12), 2637-2650.
106. Zien, A., Ratsch, G., Mika, S., Scholkopf, B., Lengauer, T., Müller, K.-R. (2000). Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics, 16(9), 799-807.