DROP: an SVM domain linker predictor trained with optimal features selected by random forest | Zendy

Teppei Ebina | Zendy; Hiroyuki Toh | Zendy; Yutaka Kuroda | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

DROP: an SVM domain linker predictor trained with optimal features selected by random forest

Author(s) -

Teppei Ebina,

Hiroyuki Toh,

Yutaka Kuroda

Publication year - 2010

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/btq700

Subject(s) - support vector machine , random forest , linker , feature selection , computer science , artificial intelligence , feature (linguistics) , drop (telecommunication) , pattern recognition (psychology) , domain (mathematical analysis) , machine learning , data mining , mathematics , mathematical analysis , philosophy , linguistics , operating system , telecommunications

Biologically important proteins are often large, multidomain proteins, which are difficult to characterize by high-throughput experimental methods. Efficient domain/boundary predictions are thus increasingly required in diverse area of proteomics research for computationally dissecting proteins into readily analyzable domains.

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