Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy.

ABSTRACT:

Background

Tyrosine sulfation is one of the most important posttranslational modifications. Due to its relevance to various disease developments, tyrosine sulfation has become the target for drug design. In order to facilitate efficient drug design, accurate prediction of sulfotyrosine sites is desirable. A predictor published seven years ago has been very successful with claimed prediction accuracy of 98%. However, it has a particularly low sensitivity when predicting sulfotyrosine sites in some newly sequenced proteins.

Results

A new approach has been developed for predicting sulfotyrosine sites using the random forest algorithm after a careful evaluation of seven machine learning algorithms. Peptides are formed by consecutive residues symmetrically flanking tyrosine sites

SUBMITTER: Yang ZR 

PROVIDER: S-EPMC2777180 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature