Fold recognition by combining profile-profile alignment and support vector machine | Zendy

Sangsoo Han | Zendy; ByungChul Lee | Zendy; Shuhui Yu | Zendy; Chan-Seok Jeong | Zendy; Suwon Lee | Zendy; Doyeon Kim | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Fold recognition by combining profile-profile alignment and support vector machine

Author(s) -

Sangsoo Han,

ByungChul Lee,

Shuhui Yu,

Chan-Seok Jeong,

Suwon Lee,

Doyeon Kim

Publication year - 2005

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/bti384

Subject(s) - support vector machine , pattern recognition (psychology) , computer science , artificial intelligence , feature vector , feature (linguistics) , algorithm , philosophy , linguistics

Currently, the most accurate fold-recognition method is to perform profile-profile alignments and estimate the statistical significances of those alignments by calculating Z-score or E-value. Although this scheme is reliable in recognizing relatively close homologs related at the family level, it has difficulty in finding the remote homologs that are related at the superfamily or fold level.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research