Modeling adaptive kernels from probabilistic phylogenetic trees

Artificial Intelligence in Medicine

Volumn 45, Issue 2-3, 2009, Pages 125-134

  Nicotra, Luca ^a   Micheli, Alessio ^a  

^a UNIVERSITY OF PISA   (Italy)

Author keywords

Bayesian networks; Fisher kernel; Gene function prediction; Kernels for structures; Phylogenetic trees; Probability product kernel

Indexed keywords

ADAPTIVE KERNELS; ADAPTIVE TREES; ADAPTIVITY; COMPARATIVE ANALYSIS; COMPUTATIONAL BIOLOGIES; FISHER KERNEL; FUNCTIONAL CLASS; GENE FUNCTION PREDICTION; GRAPHICAL MODELS; KERNEL FUNCTIONS; KERNELS FOR STRUCTURES; KEY FEATURES; OBSERVED DATUM; PHYLOGENETIC PROFILES; PHYLOGENETIC TREES; PROBABILISTIC MODELING; SACCHAROMYCES CEREVISAE; STRUCTURED DATUM; STRUCTURED MODELS; SUFFICIENT STATISTICS;

BIOINFORMATICS; DISTRIBUTED PARAMETER NETWORKS; GRAPHIC METHODS; INFERENCE ENGINES; INTELLIGENT NETWORKS; PARAMETER ESTIMATION; SPEECH ANALYSIS; SPEECH RECOGNITION; SUPPORT VECTOR MACHINES; TREES (MATHEMATICS);

BAYESIAN NETWORKS;

ARTICLE; COMPARATIVE STUDY; FISHER KERNEL; GENE FUNCTION; GENOME ANALYSIS; KERNEL METHOD; MATHEMATICAL MODEL; MOLECULAR EVOLUTION; NONHUMAN; PARAMETER; PHYLOGENETIC TREE; PRIORITY JOURNAL; PROBABILITY; PROBABILITY PRODUCT KERNEL; SACCHAROMYCES CEREVISIAE; SEQUENCE ANALYSIS; STATISTICS KERNEL; SUPPORT VECTOR MACHINE; TECHNIQUE;

ANIMALS; BAYES THEOREM; MODELS, THEORETICAL; PHYLOGENY; PROBABILITY; ROC CURVE; SACCHAROMYCES CEREVISIAE;

EID: 61449137850     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2008.08.007     Document Type: Article

References (26)

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