K-local hyperplane distance nearest-neighbor algorithm and protein fold recognition

Autor:	Oleg Okun
Rok vydání:	2006
Předmět:	Sequence Hyperplane Similarity (network science) business.industry Pattern recognition (psychology) Pattern recognition Computer Vision and Pattern Recognition Artificial intelligence business Computer Graphics and Computer-Aided Design Mathematics k-nearest neighbors algorithm
Zdroj:	Pattern Recognition and Image Analysis. 16:19-22
ISSN:	1555-6212 1054-6618
DOI:	10.1134/s1054661806010068
Popis:	Two proteins may be structurally similar but not have significant sequence similarity. Protein fold recognition is an approach usually applied in this case. It does not rely on sequence similarity and can be achieved with relevant features extracted from protein sequences. In this paper, we experiment with the K-local hyperplane distance nearest-neighbor algorithm [8] applied to the protein fold recognition and compare it with other methods. Preliminary results obtained on a real-world dataset [3] demonstrate that this algorithm can outperform many other methods tested on the same dataset.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fe78a44e0eaf4594dc21446b7dc01f38 https://doi.org/10.1134/s1054661806010068 Zobrazit plný text záznamu Full text from SpringerLink