DDPIn - Distance and density based protein indexing

10 years 7 months ago
DDPIn - Distance and density based protein indexing
Protein structure similarity and classification methods have many applications in protein function prediction and associated fields (e.g. drug discovery). In this paper, we propose a new protein structure representation method enabling fast and accurate classification. In our approach, each protein structure is represented by number of vectors (based on histogram of distances) equivalent to the number of its C residues. Each C residue represents a viewpoint from which the distances to each of the other residues are computed. Consequently, we use several methods to convert these distances into a n-dimensional feature vector which is indexed using a metric indexing structure (M-tree is the structure of our choice). While searching, we use single or multi-step approach which provides us with classification accuracy and speed comparable to the best contemporary classification methods.
David Hoksza
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2009
Authors David Hoksza
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