Abstract
In recent years, more and more attention has been paid on learning in structured domains, e.g. Chemistry. Both Neural Networks and Kernel Methods for structured data have been proposed. Here, we show that a recently developed technique for structured domains, i.e. PCA for structures, permits to generate representations of graphs (specifically, molecular graphs) which are quite effective when used for prediction tasks (QSAR studies). The advantage of these representations is that they can be generated automatically and exploited by any traditional predictor (e.g., Support Vector Regression with linear kernel).
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Gasteiger, J., Li, X., Rudolph, C., Sadowski, J., Zupan, J.: Representation of molecular electrostatic potentials by topological feature maps. Am. Chem. Soc. 116, 4608–4620 (1994)
Jacobson, K.A., Gao, Z.-G.: Adenosine receptors as therapeutic targets. Nat. Rev. Drug Discov. 5, 247–264 (2006)
Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (2002)
Schiesaro, A., Bolcato, C., Pastorin, G., Spalluto, G., Cacciari, B., Klotz, K.N., Kaseda, C., Michielan, L., Bacilieri, M., Moro, S.: Linear and non-linear 3d-qsar approaches in tandem with ligand-based homology modeling as computational strategy to depict the pyrazolo-triazolo-pyrimidine antagonists binding site of the human adenosine a2 a receptor. J. Comput. Inf. Model 48, 350–363 (2008)
Micheli, A., Sperduti, A.: Recursive principal component analysis of graphs. In: ICANN (2), pp. 826–835 (2007)
Ralaivola, L., Swamidass, S.J., Saigo, H., Baldi, P.: Graph kernels for chemical informatics. Neural Networks 18(8), 1093–1110 (2005)
Schölkopf, B., Smola, A.J., Müller, K.-R.: Kernel principal component analysis. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 583–588. Springer, Heidelberg (1997)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing 14(3), 199–222 (2004)
Sperduti, A.: Exact solutions for recursive principal components analysis of sequences and trees. In: ICANN (1), pp. 349–356 (2006)
Sperduti, A.: Efficient computation of recursive principal component analysis for structured input. In: ECML, pp. 335–346 (2007)
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Cardin, R., Michielan, L., Moro, S., Sperduti, A. (2009). PCA-Based Representations of Graphs for Prediction in QSAR Studies. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_11
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DOI: https://doi.org/10.1007/978-3-642-04277-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
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