1 |
DBLP dataset, http://dblp.uni-trier.de/xml/.
|
2 |
J. Shen, J. Zhang, X. Luo, W. Zhu, K. Yu, K. Chen, Y. Li, and H. Jiang, "Predicting protein-protein interactions based only on sequences information," Proceedings of the National Academy of Sciences, vol. 104, no. 11, pp. 4337-4341, 2007.
DOI
|
3 |
Database of Interaction Proteins, http://dip.doe-mbi.ucla.edu.
|
4 |
S. V. N. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M. Borgwardt, "Graph kernels," Journal of Machine Learning Research, vol. 11, pp. 1201-1242, 2010.
|
5 |
M. E. Newman, "Clustering and preferential attachment in growing networks," Physical Review E, vol. 64, no. 2, article ID. 025102, 2001.
DOI
|
6 |
A. L. Barabasi, H. Jeong, Z. Neda, E. Ravasz, A. Schubert, and T. Vicsek, "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and Its Applications, vol. 311, no. 3, pp. 590-614, 2002.
DOI
|
7 |
D. Liben-Nowell and J. Kleinberg, "The link prediction problem for social networks," in Proceedings of the 12th International Conference on Information and Knowledge Management (CIKM'03), New Orleans, LA, 2003, pp. 556-559.
|
8 |
M. Al Hasan, V. Chaoji, S. Salem, and M. Zaki, "Link prediction using supervised learning," in Proceedings of 4th Workshop on Link Analysis, Counter-terrorism and Security, Bethesda, MD, 2006.
|
9 |
H. Kashima and N. Abe, "A parameterized probabilistic model of network evolution for supervised link prediction," in Proceedings of 6th International Conference on Data Mining (ICDM'06), Hong Kong, 2006, pp. 340-349.
|
10 |
W. S. Noble, "Support vector machine applications in computational biology," in Kernel Methods in Computational Biology, Cambridge, MA: MIT Press, pp. 71-92, 2004.
|
11 |
Y. Guo, L. Yu, Z. Wen, and M. Li, "Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences," Nucleic Acids Research, vol. 36, no. 9, pp. 3025-3030, 2008.
DOI
|
12 |
A. Ben-Hur and W. S. Noble, "Kernel methods for predicting protein-protein interactions," Bioinformatics, vol. 21, no. suppl 1, pp. i38-i46, 2005.
DOI
|
13 |
C. N. Magnan, A. Randall, and P. Baldi, "SOLpro: accurate sequence-based prediction of protein solubility," Bioinformatics, vol. 25, no. 17, pp. 2200-2207, 2009.
DOI
|
14 |
L. Zwald, G. Blanchard, P. Massart, and R. Vert, "Kernel projection machine: a new tool for pattern recognition," Advances in Neural Information Processing Systems, vol. 17, pp. 1649-1656, 2005.
|
15 |
X. Guo and D. X. Zhou, "An empirical feature-based learning algorithm producing sparse approximations," Applied and Computational Harmonic Analysis, vol. 32, no. 3, pp. 389-400, 2012.
DOI
|
16 |
L. Zwald and G. Blanchard, "On the convergence of eigenspaces in kernel principal component analysis," Advances in Neural Information Processing Systems, vol. 18, pp. 1649-1656, 2006.
|
17 |
S. Smale and D. X. Zhou, "Online learning with Markov sampling," Analysis and Applications, vol. 7, no. 1, pp. 87-113, 2009.
DOI
|
18 |
G. Blanchard and L. Zwald, "Finite-dimensional projection for classification and statistical learning," IEEE Transactions on Information Theory, vol. 54, no. 9, pp. 4169-4182, 2008.
DOI
|
19 |
G. Raetsch, "Benchmark repository used in several Boosting, KFD, and SVM papers," Available: http://archive.ics.uci.edu/ml/datasets.html.
|