Efficient similarity search in protein structure databases by k-clique hashing | Zendy

Nils Weskamp | Zendy; Daniel Kühn | Zendy; Eyke Hüllermeier | Zendy; G. Klebe | Zendy

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Efficient similarity search in protein structure databases by k-clique hashing

Author(s) -

Nils Weskamp,

Daniel Kühn,

Eyke Hüllermeier,

G. Klebe

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bth113

Subject(s) - clique , computer science , similarity (geometry) , nearest neighbor search , hash function , locality sensitive hashing , hash table , database , information retrieval , data mining , artificial intelligence , mathematics , combinatorics , programming language , image (mathematics)

Graph-based clique-detection techniques are widely used for the recognition of common substructures in proteins. They permit the detection of resemblances that are independent of sequence or fold homologies and are also able to handle conformational flexibility. Their high computational complexity is often a limiting factor and prevents a detailed and fine-grained modeling of the protein structure.

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