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Title: Dimensionality reduction in patch-signature based protein structure matching.
Authors: Huang, Zi
Zhou, Xiaofang
Song, Dawei
Bruza, Peter D.
Editors: Dobbie, Gillian
Bailey, James
Keywords: Protein structure matching
Similarity measure
Dimensionality reduction
Issue Date: Jan-2006
Publisher: Australian Computer Society.
Citation: HUANG, Z., ZHOU, X., SONG, D. and BRUZA, P., 2006. Dimensionality reduction in patch-signature based protein structure matching. In: G. DOBBIE and J. BAILEY, eds. Database Technologies 2006: Proceedings of the 17th Australasian Database Conference (ADC2006). 16-19 January 2006. Hobart, Tasmania: Australian Computer Society, pp. 89-97.
Series/Report no.: Conferences in Research and Practice in IT (CRPIT)
Volume 49
Abstract: Searching bio-chemical structures is becoming an important application domain of information re- trieval. This paper introduces a protein structure matching problem and formulates it as an infor- mation retrieval problem. We first present a novel vector representation for protein structures, in which a protein structural region, formed by the vectors within the region, is defined as a patch and indexed by its patch signature. For a k-sized patch, its patch signature consists of 7k ¡ 10 inter-atom distances which uniquely determine the patch's spatial struc- ture. A patch matching function is then defined. As structures for proteins are large and complex, it is computationally expensive to identify possible matching patches for a given protein against a large protein database. We propose to apply dimensional- ity reduction to the patch signatures and show how the two problems are adapted to fit each other. The Locality Preservation Projection (LPP) and Singular Value Decomposition (SVD) are chosen and tested for this purpose. Experimental results show that the dimensionality reduction improves the searching speed while maintaining acceptable precision and recall. From a more general point of view, this paper demonstrates that information retrieval techniques can play a crucial role in solving this biologically critical but computationally expensive problem.
ISBN: 1920682317
Appears in Collections:Conference publications (Computing)

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