Artificial Intelligence in Medicine
Volume 45, Issue 2 , Pages 125-134 , February 2009

Modeling adaptive kernels from probabilistic phylogenetic trees

Received 7 November 2007 ,Revised 13 August 2008 ,Accepted 19 August 2008.

References 

  1. Shawe-Taylor J, Cristianini N. Kernel methods for pattern analysis. Cambridge, UK: Cambridge University Press; 2004;
  2. Schölkopf B, Tsuda K, Vert J-P. Kernel methods in computational biology. Cambridge, MA: MIT Press; 2004;
  3. Cristianini N, Kandola J, Elisee A, Shawe-Taylor J. On kernel target alignment. In:  Dietterich TG,  Becker S,  Ghahramani Z editor. Advances in neural information processing systems. vol. 14:Cambridge, MA: MIT Press; 2002;p. 367–373
  4. Micchelli CA, Pontil M. Learning the kernel function via regularization. The Journal of Machine Learning Research. 2005;6:1099–1125
  5. Ong CS, Smola A, Williamson B. Learning the kernel with hyperkernels. The Journal of Machine Learning Research. 2005;6:1043–1071
  6. Lanckriet GRG, Cristianini N, Bartlett P, Ghaoui LE, Jordan MI. Learning the kernel matrix with semidefinite programming. The Journal of Machine Learning Research. 2004;5:27–72
  7. Tsuda K, Akaho S, Asai K. The em algorithm for kernel matrix completion with auxiliary data. Journal of Machine Learning Research. 2004;4:67–81
  8. Lebanon G. Metric learning for text documents. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006;28:497–508
  9. Bianucci AM, Micheli A, Sperduti A, Starita A. Application of cascade correlation networks for structures to chemistry. Applied Intelligence. 2000;12(1/2):117–146
  10. Knudsen B, Hein J. Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucleic Acids Research. 2003;13:3423–3428
  11. Craig R, Liao L. Transductive learning with EM algorithm to classify proteins based on phylogenetic profiles. International Journal of Data Mining and Bioinformatics. 2007;337–351
  12. Liberales DA, Thoren A, von Heijne G, Eloffson A. The use of phylogenetic profiles for gene function prediction. Current Genomics. 2002;3:131–137
  13. Vert J-P. A tree kernel to analyze phylogenetic profiles. Bioinformatics. 2002;18:S276–S284
  14. Pavlidis P, Weston J, Cai J, Grundy NW. Gene functional classification from heterogeneous data. In: Proceedings of the Fifth International Conference on Computational Molecular Biology. New York: ACM Press; 2001;p. 242–248
  15. Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO. Assigning protein function by comparative genome analysis. Proceedings of the National Academy of Sciences. 1999;98(8):4285–4288
  16. Baldi P, Brunak S. Bioinformatics: the machine learning approach. 2nd ed.. Cambridge, MA: MIT Press; 2001;
  17. Nicotra L, Micheli A, Starita A. Generative kernels for Gene Function Prediction through phylogenetic tree models of evolution. In: CIBB 2007—Fourth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, No. 4578 in LNAI. Germany: Springer-Verlag; 2007;p. 512–519
  18. Jaakkola T, Haussler D. Exploiting generative models in discriminative classifiers. In:  Kearns MS,  Solla SA,  Cohn DA editor. Advances in neural information processing systems. vol. 11:Cambridge, MA: MIT Press; 1999;p. 487–493
  19. Nicotra L, Micheli A, Starita A. Fisher kernel for tree structured data. In: Proceedings of the IEEE International Joint Conference of Neural Networks. IEEE; 2004;p. 1917–1922
  20. Frasconi P, Gori M, Sperduti A. A general framework for adaptive processing of data structures. IEEE Transactions on Neural Networks. 1998;9(5):768–786
  21. Jebara T, Kondor R, Howard A. Probability product kernels. Journal of Machine Learning Research. 2004;5:819–844
  22. Narra K, Liao L. Use of extended phylogenetic profiles with E-values and support vector machines for protein family classification. International Journal of Computer and Information Sciences. 2005;6(1):56–63
  23. In:  Jordan MI editors. Learning in Graphical Models. Cambridge, MA: MIT Press; 1998;
  24. Nicotra L, Micheli A, Starita A. A comparative study of tree generative kernels for gene function prediction, Tech. Rep. TR-07–15, Computer Science Department, University of Pisa; July 2007.
  25. Marcotte EM, Pellegrini M, Thompson MJ, Yeates TO, Eisenberg D. A combined algorithm for genome-wide prediction of protein function. Nature. 1999;402:83–86
  26. Lanckriet GRG, Deng M, Cristianini N, Jordan MI, Noble WS. Kernel-based data fusion and its application to protein function prediction in yeast. In:  Altman RB,  Dunker AK,  Hunter L,  Jung TA,  Klein TE editor. Proceedings of the Pacific symposium on biocomputing. vol. 9:Singapore: World Scientific Publishing; 2004;p. 300–311

PII: S0933-3657(08)00124-3

doi: 10.1016/j.artmed.2008.08.007

Artificial Intelligence in Medicine
Volume 45, Issue 2 , Pages 125-134 , February 2009