DocumentCode
2354424
Title
Notice of Violation of IEEE Publication Principles
Wavelet Assignment Graph Kernel for Drug Virtual Screening
Author
Soman, Soumya T. ; Soman, K.P.
Author_Institution
Comput. Eng. & Networking, Coimbatore, India
fYear
2009
fDate
27-28 Oct. 2009
Firstpage
282
Lastpage
284
Abstract
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.
"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.
Keywords
feature extraction; graph theory; pharmaceutical industry; toxicology; virtual reality; wavelet transforms; chemical compounds; drug discovery; drug virtual screening; feature extraction; graph classification; predictive toxicology challenge data set; wavelet assignment graph kernel; Chemical analysis; Chemical compounds; Chemical technology; Computer networks; Drugs; Kernel; Support vector machine classification; Support vector machines; Toxicology; Wavelet analysis; QSAR; SVM; kernel; wavelet;
fLanguage
English
Publisher
ieee
Conference_Titel
Advances in Recent Technologies in Communication and Computing, 2009. ARTCom '09. International Conference on
Conference_Location
Kottayam, Kerala
Print_ISBN
978-1-4244-5104-3
Type
conf
DOI
10.1109/ARTCom.2009.197
Filename
5329479
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