A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics | Zendy

BobbieJo WebbRobertson | Zendy; William R. Can | Zendy; Christopher Oehmen | Zendy; Anuj Shah | Zendy; Vidhya Gurumoorthi | Zendy; Mary Lipton | Zendy; Katrina M. Waters | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics

Author(s) -

BobbieJo WebbRobertson,

William R. Can,

Christopher Oehmen,

Anuj Shah,

Vidhya Gurumoorthi,

Mary Lipton,

Katrina M. Waters

Publication year - 2008

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/btn218

Subject(s) - support vector machine , computer science , mass spectrometry , software , tandem mass spectrometry , shewanella oneidensis , computational biology , data mining , proteomics , artificial intelligence , chemistry , chromatography , biology , biochemistry , gene , programming language , bacteria , genetics

The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic).

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