Conference Paper

Protein Classification with Kernelized Softassign.

DOI: 10.1007/978-3-540-31988-7_32 Conference: Graph-Based Representations in Pattern Recognition, 5th IAPR InternationalWorkshop, GbRPR 2005, Poitiers, France, April 11-13, 2005, Proceedings
Source: DBLP


In this paper we address the problem of comparing and classifying protein surfaces through a kernelized version of the Softassign
graph-matching algorithm. Preliminary experiments with random-generated graphs have suggested that weighting the quadratic
cost function of Softassign with information coming from the computation of diffusion kernels on graphs attenuate the performance
decay with increasing noise levels. Our experimental results show that this approach yields a useful similarity measure to
cluster proteins with similar structure, to automatically find prototypical graphs representing families of proteins and also
to classify proteins in terms of their distance to these prototypes. We also show that the role of kernel-based information
is to smooth the obtained matching fields, which in turn results in noise-free prototype estimation.

3 Reads

Similar Publications