NDLI: Graph methods for predicting the function of chemical compounds

Content Provider	IEEE Xplore Digital Library
Author	Ya Zhu Changhui Yan
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., North Dakota State Univ., Fargo, ND, USA (Ya Zhu; Changhui Yan)
Abstract	Finding chemical compounds that can be used to treat a certain disease has long been a focus of the biomedical research. Using traditional laboratory approaches, scientists have to test numerous chemical compounds in order to find a drugable compound. Computational methods can speed up this screening process. We compared various computational methods that predict the function of chemical compounds. The global similarity methods used graph kernels to enumerate and compare various structural components in compounds. The local similarity methods looked for common subgraphs that drugable compounds share. We evaluated these two categories of methods using five benchmark datasets from the National Cancer Institute database. Our results showed that the MinMax Tanimoto kernel was better than other graph kernels in predicting the function of chemical compounds. Our results also showed that local similarity methods outperformed the global similarity, which suggested that local substructures played a pivotal role in the function of chemical compounds. We explored different ensemble methods to integrate the predictions from global and local similarity methods. Our results showed that combining global and local similarity methods significantly improved the prediction performance, which indicated that the global and local similarity methods complemented each other in comparing structural similarity. Our ensemble method achieved better performance than other state-of-the-art methods in direct comparisons.
Starting Page	386
Ending Page	390
File Size	1864627
Page Count	5
File Format	PDF
ISBN	9781479954643
DOI	10.1109/GRC.2014.6982869
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-22
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	maximum common subgraphs Conferences Support vector machine classification Vegetation graph kernels ensemble Compounds function prediction Kernel Chemical compounds chemical compounds
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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