Notice of Violation of IEEE Publication PrinciplesWavelet Assignment Graph Kernel for Drug Virtual Screening

Notice of Violation of IEEE Publication Principles

"Wavelet Assignment Graph Kernal for Drug Virtual Screening"
by Soumya T. Soman and Soman K.P.
in the Proceedings of the 2009 International Conference on Advances in Recent Technology in Communication and Computing, pp 282-284

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"Graph Wavelet Alignment Kernals for Drug Virtual Screening"
by A. Smalter, J. Huan, G. Lushington
in the Proceedings of the 7th Annual International Conference on Computational Systems Bioinformatic, Life Sciences Society, August 2008, pp. 327-338

We propose a kernel function called wavelet assignment graph kernel for graph classification which has applications in drug discovery. This is an extension of wavelet alignment graph kernel. In this method we use graphs to model chemical compounds. For feature extraction we have applied wavelet analysis to graph structured chemical structure, for each atom we collect features about the atom and its local environment with different scales. For finding the similarity between two graphs, nodes of one graph are aligned with nodes of the other graph. such that total overall similarity is maximized with respect to all possible alignment. For alignment between two graphs we have used Wavelet Assignment graph kernel. We have evaluated the efficiency of our kernel function using Predictive Toxicolog- Challenge data set. Our results indicate that the new kernel function is more efficient than the existing wavelet alignment graph kernel function.