Kernel Methods in Bioinformatics

  • Karsten M. BorgwardtEmail author
Part of the Springer Handbooks of Computational Statistics book series (SHCS)


Kernel methods have now witnessed more than a decade of increasing popularity in the bioinformatics community. In this article, we will compactly review this development, examining the areas in which kernel methods have contributed to computational biology and describing the reasons for their success.


Support Vector Machine Kernel Method Prediction Task Multiple Kernel Learning Protein Function Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403–410.Google Scholar
  2. 2.
    Ben-Hur, A., & Brutlag, D. (2003). Remote homology detection: A motif based approach. Bioinformatics, 19 (Suppl. 1), i26–i33. URL PMID: 12855434
  3. 3.
    Ben-Hur, A., & Noble, W. S. (2005). Kernel methods for predicting protein-protein interactions. Bioinformatics (Oxford, England), 21 (Suppl. 1), i38–i46. DOI10.1093/bioinformatics/bti1016. URL PMID: 15961482
  4. 4.
    Ben-Hur, A., Ong, C. S., Sonnenburg, S., Schölkopf, B., & Rätsch, G. (2008). Support vector machines and kernels for computational biology. PLoS Computational Biology, 4(10), e1000,173. DOI10.1371/journal.pcbi.1000173. URL PMID: 18974822
  5. 5.
    Bock, J. R., & Gough, D. A. (2001). Predicting protein–protein interactions from primary structure. Bioinformatics (Oxford, England), 17(5), 455–460. URL PMID: 11331240Google Scholar
  6. 6.
    Bona, F. D., Ossowski, S., Schneeberger, K., & Rätsch, G. (2008). Optimal spliced alignments of short sequence reads. Bioinformatics (Oxford, England), 24(16), i174–i180. DOI10.1093/bioinformatics/btn300. URL PMID: 18689821
  7. 7.
    Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H. P., Schölkopf, B., & Smola, A. J. (2006). Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics (ISMB), 22(14), e49–e57.CrossRefGoogle Scholar
  8. 8.
    Borgwardt, K. M., & Kriegel, H. P. (2005). Shortest-path kernels on graphs. In ICDM (pp. 74–81). IEEE Computer Society.Google Scholar
  9. 9.
    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(Suppl 1), i47–i56.CrossRefGoogle Scholar
  10. 10.
    Borgwardt, K. M., Vishwanathan, S. V. N., & Kriegel, H. P. (2006). Class prediction from time series gene expression profiles using dynamical systems kernels. In R. B. Altman, T. Murray, T. E. Klein, A. K. Dunker, & L. Hunter (Eds.), Pacific symposium on biocomputing (pp. 547–558). World Scientific.Google Scholar
  11. 11.
    Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In D. Haussler (Ed.), Proceedings of the annual conference on computational learning theory (pp. 144–152). Pittsburgh, PA: ACM.Google Scholar
  12. 12.
    Brown, M. P. S., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C., Furey, T. S., et al. (2000). Knowledge-based analysis of microarray gene expression data using support vector machines. Proceedings of the National Academy of Sciences of the United States of America, 97(1), 262–267.CrossRefGoogle Scholar
  13. 13.
    Cai, Y. D., Liu, X. J., Xu, X. B., & Chou, K. C. (2002). Prediction of protein structural classes by support vector machines. Computational Chemistry, 26(3), 293–296.CrossRefGoogle Scholar
  14. 14.
    Ding, C. H., & Dubchak, I. (2001). Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 17(4), 349–358.CrossRefGoogle Scholar
  15. 15.
    Dobson, P. D., & Doig, A. J. (2003). Distinguishing enzyme structures from non-enzymes without alignments. Journal of Molecular Biology, 330(4), 771–783.CrossRefGoogle Scholar
  16. 16.
    Durbin, R., Eddy, S., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge, UK: Cambridge University Press.zbMATHCrossRefGoogle Scholar
  17. 17.
    Gärtner, T., Flach, P. A., & Wrobel, S. (2003). On graph kernels: Hardness results and efficient alternatives. In B. Schölkopf & M. K. Warmuth (Eds.), COLT, Lecture Notes in Computer Science (Vol. 2777, pp. 129–143). Springer.Google Scholar
  18. 18.
    Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., et al. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286(5439), 531–537.CrossRefGoogle Scholar
  19. 19.
    Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., & Smola, A. (2007). A kernel method for the two-sample-problem. In Advances in neural information processing systems (Vol. 19, pp. 513–520). Cambridge, MA: MIT.Google Scholar
  20. 20.
    Gretton, A., Fukumizu, K., Teo, C. H., Song, L., Schölkopf, B., & Smola, A. J. (2007). A kernel statistical test of independence. In J. C. Platt, D. Koller, Y. Singer, & S. T. Roweis (Eds.), NIPS. MIT Press.Google Scholar
  21. 21.
    Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46, 389–422.zbMATHCrossRefGoogle Scholar
  22. 22.
    Haussler, D. (1999). Convolutional kernels on discrete structures. Tech. Rep., UCSC-CRL-99-10. UC Santa Cruz: Computer Science Department.Google Scholar
  23. 23.
    Henikoff, S., Henikoff, J. G. (1991). Automated assembly of protein blocks for database searching. Nucleic Acids Research, 19, 6565–6572.CrossRefGoogle Scholar
  24. 24.
    Horváth, T., Gärtner, T., & Wrobel, S. (2004). Cyclic pattern kernels for predictive graph mining. In W. Kim, R. Kohavi, J. Gehrke, & W. DuMouchel (Eds.), KDD (pp. 158–167). ACM.Google Scholar
  25. 25.
    Hua, S., & Sun, Z. (2001). 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. DOI10.1006/jmbi.2001.4580. URL PMID: 11327775
  26. 26.
    Imrich, W., & Klavzar, S. (2000). Product graphs: Structure and recognition. In Wiley Interscience Series in Discrete Mathematics. New York: Wiley VCH.Google Scholar
  27. 27.
    Jaakkola, T., Diekhans, M., & Haussler, D. (1999). Using the fisher kernel method to detect remote protein homologies. In T. Lengauer, R. Schneider, P. Bork, D. L. Brutlag, J. I. Glasgow, H. W. Mewes, et al. (Eds.), ISMB (pp. 149–158). AAAI.Google Scholar
  28. 28.
    Kashima, H., Tsuda, K., & Inokuchi, A. (2003). Marginalized kernels between labeled graphs. In Proceedings of the 20th International Conference on Machine Learning (ICML). Washington, DC: United States.Google Scholar
  29. 29.
    Kato, T., Tsuda, K., & Asai, K. (2005). Selective integration of multiple biological data for supervised network inference. Bioinformatics (Oxford, England), 21(10), 2488–2495. DOI10.1093/bioinformatics/bti339. URL PMID: 15728114
  30. 30.
    Kawashima, S., Ogata, H., & Kanehisa, M. (1999). Aaindex: Amino acid index database. Nucleic Acids Research, 27(1), 368–369.CrossRefGoogle Scholar
  31. 31.
    Kim, S., Nam, J., Rhee, J., Lee, W., & Zhang, B. (2006). miTarget: microRNA target gene prediction using a support vector machine. BMC Bioinformatics, 7, 411. DOI10.1186/1471-2105-7-411. URL PMID: 16978421
  32. 32.
    Kuksa, P. P., Huang, P. H., & Pavlovic, V. (2008). Scalable algorithms for string kernels with inexact matching. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), NIPS (pp. 881–888). MIT.Google Scholar
  33. 33.
    Lafferty, J. D., McCallum, A., & Pereira, F. C. N. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In C. E. Brodley & A. P. Danyluk (Eds.), ICML (pp. 282–289). Morgan Kaufmann.Google Scholar
  34. 34.
    Lanckriet, G., Cristianini, N., Bartlett, P., Ghaoui, L. E., & Jordan, M. I. (2004). Learning the kernel matrix with semi-definite programming. Journal of Machine Learning Research, 5, 27–72.zbMATHGoogle Scholar
  35. 35.
    Lanckriet, G. R. G., Bie, T. D., Cristianini, N., Jordan, M. I., & Noble, W. S. (2004). A statistical framework for genomic data fusion. Bioinformatics, 20(16), 2626–2635. DOI10.1093/bioinformatics/bth294. URL PMID: 15130933Google Scholar
  36. 36.
    Leslie, C., Eskin, E., & Noble, W. S. (2002). The spectrum kernel: A string kernel for SVM protein classification. In Proceedings of the pacific symposium on biocomputing (pp. 564–575).Google Scholar
  37. 37.
    Leslie, C., Eskin, E., Weston, J., & Noble, W. S. (2002). Mismatch string kernels for SVM protein classification. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems (Vol. 15). Cambridge, MA: MIT.Google Scholar
  38. 38.
    Leslie, C. S., & Kuang, R. (2003). Fast kernels for inexact string matching. In B. Schölkopf & M. K. Warmuth (Eds.), COLT, Lecture Notes in Computer Science (Vol. 2777, pp. 114–128). Springer.Google Scholar
  39. 39.
    Leslie, C. S., Eskin, E., Cohen, A., Weston, J., & Noble, W. S. (2004). Mismatch string kernels for discriminative protein classification. Bioinformatics (Oxford, England), 20(4), 467–476. DOI10.1093/bioinformatics/btg431. URL PMID: 14990442
  40. 40.
    Lewis, D. P., Jebara, T., & Noble, W. S. (2006). Support vector machine learning from heterogeneous data: An empirical analysis using protein sequence and structure. Bioinformatics (Oxford, England), 22(22), 2753–2760. DOI10.1093/bioinformatics/btl475. URL PMID: 16966363
  41. 41.
    Liao, L., & Noble, W. S. (2002). Combining pairwise sequence similarity and support vector machines for remote protein homology detection. In RECOMB (pp. 225–232).Google Scholar
  42. 42.
    Liu, J., Gough, J., & Rost, B. (2006). Distinguishing Protein-Coding from Non-Coding RNAs through support vector machines. PLoS Genetics, 2(4), 529–536.CrossRefGoogle Scholar
  43. 43.
    Logan, B., Moreno, P., Suzek, B., Weng, Z., & Kasif, S. (2001). A study of remote homology detection. Tech. Rep., Cambridge Research Laboratory.Google Scholar
  44. 44.
    Matsuda, S., Vert, J., 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: A Publication of the Protein Society, 14(11), 2804–2813. DOI10.1110/ps.051597405. URL PMID: 16251364
  45. 45.
    Mewes, H. W., Frishman, D., Gruber, C., Geier, B., Haase, D., Kaps, A., et al. (2000). MIPS: A database for genomes and protein sequences. Nucleic Acids Research, 28(1), 37–40. URL PMID: 10592176
  46. 46.
    Mukherjee, S., Tamayo, P., Slonim, D., Verri, A., Golub, T., Mesirov, J.P., et al. (2000). Support vector machine classification of microarray data. Tech. Rep., Artificial Intelligence Laboratory, Massachusetts Institute of Technology.Google Scholar
  47. 47.
    Murzin, A. G., Brenner, S. E., Hubbard, T., & Chothia, C. (1995). SCOP: A structural classification of proteins database for the investigation of sequences and structures. Journal of Molecular Biology, 247(4), 536–40. DOI10.1006/jmbi.1995.0159. URL PMID: 7723011
  48. 48.
    Noble, W. (2004). Support vector machine applications in computational biology. In B. Schölkopf, K. Tsuda, & J. P. Vert (Eds.), Kernel methods in computational biology. Cambridge, MA: MIT.Google Scholar
  49. 49.
    Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567. DOI10.1038/nbt1206-1565. URL Google Scholar
  50. 50.
    Ong, C. S., & Smola, A. J. (2003). Machine learning with hyperkernels. In T. Fawcett & N. Mishra (Eds.), ICML (pp. 568–575). AAAI.Google Scholar
  51. 51.
    Ortiz, A. R., Strauss, C. E. M., & Olmea, O. (2002). MAMMOTH (matching molecular models obtained from theory): An automated method for model comparison. Protein Science: A Publication of the Protein Society, 11(11), 2606–2621. DOI10.1110/ps.0215902. URL PMID: 12381844
  52. 52.
    Qiu, J., Hue, M., Ben-Hur, A., Vert, J., & Noble, W. S. (2007). A structural alignment kernel for protein structures. Bioinformatics (Oxford, England), 23(9), 1090–1098. DOI10.1093/bioinformatics/btl642. URL PMID: 17234638Google Scholar
  53. 53.
    Qiu, J., & Noble, W. S. (2008). Predicting co-complexed protein pairs from heterogeneous data. PLoS Computational Biology, 4(4), e1000,054. DOI10.1371/journal.pcbi.1000054. URL PMID: 18421371
  54. 54.
    Rakotomamonjy, A., Bach, F., Canu, S., & Grandvalet, Y. (2007). More efficiency in multiple kernel learning. In Z. Ghahramani (Ed.), ICML, ACM International Conference Proceeding Series (Vol. 227, pp. 775–782). ACM.Google Scholar
  55. 55.
    Ramon, J., & Gärtner, T. (2003). Expressivity versus efficiency of graph kernels. Tech. Rep., First International Workshop on Mining Graphs, Trees and Sequences (held with ECML/PKDD’03).Google Scholar
  56. 56.
    Rätsch, G., Sönnenburg, S., & Schölkopf, B. (2005). RASE: Recognition of alternatively spliced exons in c. elegans. Bioinformatics, 21 (Suppl. 1), i369–i377.Google Scholar
  57. 57.
    Rätsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., Müller, K., Sommer, R., et al. (2007). Improving the Caenorhabditis elegans genome annotation using machine learning. PLoS Computational Biology, 3(2), e20. PMID: 17319737CrossRefGoogle Scholar
  58. 58.
    Sakakibara, Y., Popendorf, K., Ogawa, N., Asai, K., & Sato, K. (2007). Stem kernels for RNA sequence analyses. Journal of Bioinformatics and Computational Biology, 5(5), 1103–1122. URL PMID: 17933013Google Scholar
  59. 59.
    Sato, K., Mituyama, T., Asai, K., & Sakakibara, Y. (2008). Directed acyclic graph kernels for structural RNA analysis. BMC Bioinformatics, 9, 318. DOI10.1186/1471-2105-9-318. URL PMID: 18647390
  60. 60.
    Schölkopf, B. (1997). Support vector learning. München: R. Oldenbourg Verlag. PhD thesis, TU Berlin. Download:
  61. 61.
    Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels. Cambridge, MA: MIT.Google Scholar
  62. 62.
    Schölkopf, B., Smola, A. J., & Müller, K. R. (1997). Kernel principal component analysis. In W. Gerstner, A. Germond, M. Hasler, & J. D. Nicoud (Eds.), Artificial neural networks ICANN’97 (Vol. 1327, pp. 583–588). Berlin: Springer Lecture Notes in Computer Science.CrossRefGoogle Scholar
  63. 63.
    Schölkopf, B., Tsuda, K., & Vert, J. P. (2004). Kernel Methods in Computational Biology. Cambridge, MA: MIT.Google Scholar
  64. 64.
    Schultheiss, S. J., Busch, W., Lohmann, J. U., Kohlbacher, O., & Rätsch, G. (2009). KIRMES: kernel-based identification of regulatory modules in euchromatic sequences. Bioinformatics (Oxford, England), DOI10.1093/bioinformatics/btp278. URL PMID: 19389732
  65. 65.
    Schulze, U., Hepp, B., Ong, C. S., & Rätsch, G. (2007). PALMA: mRNA to genome alignments using large margin algorithms. Bioinformatics (Oxford, England), 23(15), 1892–1900.DOI10.1093/bioinformatics/btm275. URL PMID: 17537755
  66. 66.
    Schweikert, G., Zien, A., Zeller, G., Behr, J., Dieterich, C., Ong, C. S., et al. (2009). mGene: Accurate SVM-based gene finding with an application to nematode genomes. Genome Research, 19(11), 2133–2143. DOI10.1101/gr.090597.108. URL PMID: 19564452
  67. 67.
    Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  68. 68.
    Shervashidze, N., & Borgwardt, K. M. (2009). Fast subtree kernels on graphs. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, & A. Culotta (Eds.), NIPS (pp. 1660–1668). Cambridge, MA: MIT.Google Scholar
  69. 69.
    Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., & Borgwardt, K. M. (2009). Efficient graphlet kernels for large graph comparison. In D. van Dyk & M. Welling (Eds.), Proceedings of the twelfth international conference on artificial intelligence and statistics. Clearwater Beach, Florida.Google Scholar
  70. 70.
    Smith, T. F., & Waterman, M. S. (1981). Identification of common molecular subsequences. Journal of Molecular Biology, 147(1), 195–197. URL PMID: 7265238Google Scholar
  71. 71.
    Song, L., Bedo, J., Borgwardt, K., Gretton, A., & Smola, A. (2007). Gene selection via the BAHSIC family of algorithms. Bioinformatics, 23(13), i490–i498.CrossRefGoogle Scholar
  72. 72.
    Song, L., Smola, A., Gretton, A., Borgwardt, K., & Bedo, J. (2007). Supervised feature selection via dependence estimation. In: Ghahramani, Z. (ed.): ACM International Conference Proceeding Series, vol. 227. ACM.Google Scholar
  73. 73.
    Sonnenburg, S., Rätsch, G., Jagota, A. K., & Müller, K. R. (2002). New methods for splice site recognition. In Proceedings of the International Conference on Artificial Neural Networks (ICANN) (pp. 329–336).Google Scholar
  74. 74.
    Sonnenburg, S., Rätsch, G., & Rieck, K. (2007). Large-scale learning with string kernels. In L. Bottou, O. Chapelle, D. DeCoste, & J. Weston (Eds.), Large-Scale kernel machines (pp. 73—104). Cambridge, MA: MIT.Google Scholar
  75. 75.
    Sonnenburg, S., Rätsch, G., & Schäfer, C. (2005). A general and efficient multiple kernel learning algorithm. In NIPS.Google Scholar
  76. 76.
    Sonnenburg, S., Rätsch, G., & Schäfer, C. (2005). Learning interpretable SVMs for biological sequence classification. In RECOMB 2005, LNBI 3500 (pp. 389–407). Berlin, Heidelberg: Springer-Verlag.Google Scholar
  77. 77.
    Sonnenburg, S., Zien, A., Philips, P., & Rätsch, G. (2008). POIMs: positional oligomer importance matrices — understanding support vector machine based signal detectors. Bioinformatics, 24(13), i6–i14. URL
  78. 78.
    Sonnenburg, S., Zien, A., & Rätsch, G. (2006). ARTS: Accurate recognition of tran- scription starts in human. Bioinformatics (Oxford, England)22(14), e472–480. DOI10.1093/ DOIbioinformatics/btl250. URL PMID: 16873509
  79. 79.
    Steinwart, I. (2002). Support vector machines are universally consistent. Journal of Complexity, 18, 768–791.MathSciNetzbMATHCrossRefGoogle Scholar
  80. 80.
    Su, Q. J., Lu, L., Saxonov, S., & Brutlag, D. L. (2005). eBLOCKs: Enumerating conserved protein blocks to achieve maximal sensitivity and specificity. Nucleic Acids Research, 33(Database issue), D178–D182. DOI10.1093/nar/gki060. URL PMID: 15608172
  81. 81.
    Tsochantaridis, I., Joachims, T., Hofmann, T., & Altun, Y. (2005). Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6, 1453–1484.MathSciNetzbMATHGoogle Scholar
  82. 82.
    Tsuda, K., Kin, T., & Asai, K. (2002). Marginalized kernels for biological sequences. Bioinformatics (Oxford, England), 18 (Suppl. 1), S268–S275. URL PMID: 12169556
  83. 83.
    Tsuda, K., Noble, W. S. (2004). Learning kernels from biological networks by maximizing entropy. Bioinformatics (Oxford, England), 20 (Suppl. 1), i326–i333. DOI10.1093/bioinformatics/bth906. URL PMID: 15262816
  84. 84.
    Tsuda, K., Shin, H., & Schölkopf, B. (2005). Fast protein classification with multiple networks. Bioinformatics, 21 (Suppl. 2), ii59–ii65.Google Scholar
  85. 85.
    Vapnik, V. (1998). Statistical learning theory. New York: Wiley.zbMATHGoogle Scholar
  86. 86.
    Vert, J. (2002). A tree kernel to analyse phylogenetic profiles. Bioinformatics, 18, S276–S284.CrossRefGoogle Scholar
  87. 87.
    Vert, J., Qiu, J., & Noble, W. S. (2007). A new pairwise kernel for biological network inference with support vector machines. BMC Bioinformatics, 8 (Suppl. 10), S8. DOI10.1186/1471-2105-8-S10-S8. URL PMID: 18269702
  88. 88.
    Vert, J. P., Saigo, H., & Akutsu, T. (2004). Local alignment kernels for biological sequences. In B. Schölkopf, K. Tsuda, & J. P. Vert (Eds.), Kernel methods in computational biology (pp. 261–274). Cambridge, MA: MIT.Google Scholar
  89. 89.
    Vishwanathan, S., & Smola, A. (2003). Fast kernels for string and tree matching. In K. Tsuda, B. Schölkopf, & J. Vert (Eds.), Kernels and bioinformatics. Cambridge, MA: MIT. ForthcomingGoogle Scholar
  90. 90.
    Vishwanathan, S. V., Smola, A. J., & Vidal, R. (2007). Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes. International Journal of Computer Vision, 73(1), 95–119. URL Google Scholar
  91. 91.
    Vishwanathan, S. V. N., Borgwardt, K., & Schraudolph, N. N. (2007). Fast computation of graph kernels. In B. Schölkopf, J. Platt, & T. Hofmann (Eds.), Advances in neural information processing systems (Vol. 19). Cambridge MA: MIT.Google Scholar
  92. 92.
    Wang, X., & Naqa, I. M. E. (2008). Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics (Oxford, England), 24(3), 325–332. DOI10.1093/bioinformatics/btm595. URL PMID: 18048393
  93. 93.
    Weinberger, K. Q., Sha, F., & Saul, L. K. (2004). Learning a kernel matrix for nonlinear dimensionality reduction. In Proceedings of the 21st international conference on machine learning. Banff, Canada.Google Scholar
  94. 94.
    Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., & Vapnik, V. (2000). Feature selection for svms. In T. K. Leen, T. G. Dietterich, V. Tresp (Eds.), NIPS (pp. 668–674). MIT.Google Scholar
  95. 95.
    Yamanishi, Y., Vert, J., & Kanehisa, M. (2004). Protein network inference from multiple genomic data: A supervised approach. Bioinformatics (Oxford, England), 20 (Suppl. 1), i363–i370. DOI10.1093/bioinformatics/bth910. URL PMID: 15262821
  96. 96.
    Yamanishi, Y., Vert, J., & Kanehisa, M. (2005). Supervised enzyme network inference from the integration of genomic data and chemical information. Bioinformatics (Oxford, England), 21 (Suppl 1), i468–i477. DOI10.1093/bioinformatics/bti1012. URL PMID: 15961492
  97. 97.
    Zeller, G., Clark, R. M., Schneeberger, K., Bohlen, A., Weigel, D., & Rätsch, G. (2008). Detecting polymorphic regions in Arabidopsis thaliana with resequencing microarrays. Genome Research, 18(6), 918–929.CrossRefGoogle Scholar
  98. 98.
    Zeller, G., Henz, S. R., Laubinger, S., Weigel, D., & Rätsch, G. (2008). Transcript normalization and segmentation of tiling array data. In R. B. Altman, A. K. Dunker, L. Hunter, T. Murray, & T.E. Klein (Eds.), Pacific symposium on biocomputing (pp. 527–538). World Scientific.Google Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Machine Learning and Computational Biology Research GroupMax Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, TübingenTübingenGermany

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