Graph kernels based on tree patterns for molecules

Machine Learning

Volumn 75, Issue 1, 2009, Pages 3-35

  Mahé, Pierre ^a   Vert, Jean Philippe ^a,b,c  

^a PSL RESEARCH UNIVERSITY   (France)

^b INSTITUT CURIE   (France)

^c INSERM U900   (France)

Author keywords

Chemoinformatics; Graph kernels; Support vector machines

Indexed keywords

GALLIUM; IMAGE RETRIEVAL; SUPPORT VECTOR MACHINES;

ANTI-CANCER ACTIVITIES; CHEMICAL APPLICATIONS; CHEMOINFORMATICS; FEATURE SPACES; GRAPH KERNELS; GRAPH REPRESENTATIONS; INTERNATIONAL WORKSHOPS; MODULAR EXTENSIONS; POSITIVE DEFINITE KERNELS; SMALL MOLECULES; SUBTREES; TREE PATTERNS;

TREES (MATHEMATICS);

EID: 60949112299     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-008-5086-2     Document Type: Article

References (20)

1. K. M. Borgwardt C. S. Ong S. Schönauer S. V. N. Vishwanathan A. J. Smola H.-P. Kriegel 2005 Protein function prediction via graph kernels Bioinformatics 21 Suppl. 1 i47 i56
2. Chang, C.-C., & Lin, C.-J. (2001). LIBSVM: a library for support vector machines.
3. J. Chen S. J. Swamidass Y. Dou J. Bruand P. Baldi 2005 ChemDB: a public database of small molecules and related chemoinformatics resources Bioinformatics 21 22 4133 4139
4. M. Deshpande M. Kuramochi N. Wale G. Karypis 2005 Frequent substructure-based approaches for classifying chemical compounds IEEE Transactions on Knowledge and Data Engineering 17 8 1036 1050
5. Gärtner, T., Flach, P., & Wrobel, S. (2003). On graph kernels: hardness results and efficient alternatives. In B. Schölkopf & M. Warmuth (Eds.), Lecture notes in computer science : Vol. 2777. Proceedings of the sixteenth annual conference on computational learning theory and the seventh annual workshop on kernel machines (pp. 129-143). Heidelberg: Springer.
6. C. Helma T. Cramer S. Kramer L. De Raedt 2004 Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds Journal of Chemical Information and Computer Sciences 44 4 1402 1411
7. Horváth, T., Gärtner, T., & Wrobel, S. (2004). Cyclic pattern kernels for predictive graph mining. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 158-167). New York: ACM Press.
8. A. Inokuchi T. Washio H. Motoda 2003 Complete mining of frequent patterns from graphs: mining graph data Machine Learning 50 3 321 354
9. 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).
10. Kashima, H., Tsuda, K., & Inokuchi, A. (2004). Kernels for graphs. In B. Schölkopf, K. Tsuda, & J. P. Vert (Eds.), Kernel methods in computational biology (pp. 155-170). Cambridge: MIT Press.
11. R. D. King S. H. Muggleton A. Srinivasan M. J. Sternberg 1996 Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming Proceedings of the National Academy of Sciences of the United States of America 93 1 438 442
12. Leach, A. R., & Gillet, V. J. (2003). An introduction to chemoinformatics. Dordrecht: Kluwer Academic.
13. Leslie, C., Eskin, E., & Noble, W. S. (2002). The spectrum kernel: a string kernel for SVM protein classification. In R. B. Altman, A. K. Dunker, L. Hunter, K. Lauerdale, & T. E. Klein (Eds.), Proceedings of the Pacific symposium on biocomputing 2002 (pp. 564-575). Singapore: World Scientific.
14. P. Mahé N. Ueda T. Akutsu J.-L. Perret J.-P. Vert 2005 Graph kernels for molecular structure-activity relationship analysis with support vector machines Journal of Chemical Information and Modeling 45 4 939 951
15. Menchetti, S., Costa, F., & Frasconi, P. (2005). Weighted decomposition kernels. In L. De Raedt & S. Wrobel (Eds.), Proceedings of the twenty-second international conference on machine learning (ICML 2005) (pp. 585-592). New York: Assoc. Comput. Mach.
16. L. Ralaivola S. J. Swamidass H. Saigo P. Baldi 2005 Graph kernels for chemical informatics Neural Networks 18 8 1093 1110
17. Ramon, J., & Gärtner, T. (2003). Expressivity versus efficiency of graph kernels. In T. Washio & L. De Raedt (Eds.), Proceedings of the first international workshop on mining graphs, trees and sequences (pp. 65-74).
18. Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge: MIT Press.
19. Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge: Cambridge University Press.
20. S. J. Swamidass J. Chen J. Bruand P. Phung L. Ralaivola P. Baldi 2005 Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity Bioinformatics 21 Suppl. 1 i359 i368