Accurate extraction of functional associations between proteins based on common interaction partners and common domains | Zendy

Keita Okada | Zendy; Shigehiko Kanaya | Zendy; Kiyoshi Asai | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Accurate extraction of functional associations between proteins based on common interaction partners and common domains

Author(s) -

Keita Okada,

Shigehiko Kanaya,

Kiyoshi Asai

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/bti305

Subject(s) - false positive paradox , computational biology , function (biology) , computer science , domain (mathematical analysis) , protein function prediction , task (project management) , protein function , data mining , protein–protein interaction , bioinformatics , biology , machine learning , genetics , mathematics , gene , mathematical analysis , management , economics

Genomic and proteomic approaches have accumulated a huge amount of data which provide clues to protein function. However, interpreting single omic data for predicting uncharacterized protein functions has been a challenging task, because the data contain a lot of false positives. To overcome this problem, methods for integrating data from various omic approaches are needed for more accurate function prediction.

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