DocumentCode :
186058
Title :
Graph methods for predicting the function of chemical compounds
Author :
Ya Zhu ; Changhui Yan
Author_Institution :
Dept. of Comput. Sci., North Dakota State Univ., Fargo, ND, USA
fYear :
2014
fDate :
22-24 Oct. 2014
Firstpage :
386
Lastpage :
390
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.
Keywords :
cancer; chemical engineering computing; patient treatment; pharmaceutical industry; MinMax Tanimoto kernel; National Cancer Institute database; biomedical research; chemical compounds; drugable compound; global similarity methods; graph methods; local similarity methods; Chemical compounds; Compounds; Conferences; Kernel; Support vector machine classification; Vegetation; chemical compounds; ensemble; function prediction; graph kernels; maximum common subgraphs;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing (GrC), 2014 IEEE International Conference on
Conference_Location :
Noboribetsu
Type :
conf
DOI :
10.1109/GRC.2014.6982869
Filename :
6982869
Link To Document :
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