Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5506
Title: A graph-based pattern recognition for chemical molecule matching
Authors: Gökçer, Türkan Yeliz
Demirci, Muhammed Fatih
Tan, M.
Keywords: Bioinformatics
Chemical molecule matching
Classification
Graph matching
Pattern recognition
Issue Date: 2015
Publisher: SciTePress
Source: 6th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2015, 12 January 2015 through 15 January 2015, , 112651
Abstract: In this paper we present a new method that uses graph-based pattern recognition to compute the similarity between chemical molecules. Our method is used for prediction of the activity of chemical molecules, that is, the prediction of carcinogenicity of molecules. In our method, molecules are depicted as edge-weighted graphs, where each atom corresponds to a vertex and the bonds between the atoms are depicted as edges. The framework performs graph embedding by representing vertices as points in a geometric space. The similarity measure (distance) between the embedded points is computed using the Earth Mover's Distance (EMD) method, which is based on a distribution-based transportation algorithm. Our method shows promising results on the PTC dataset compared to the existing kernels.
URI: https://hdl.handle.net/20.500.11851/5506
ISBN: 9789897580703
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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