Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy.
Yang, ZR
Date: 2009
Journal
BMC Bioinformatics
Publisher
BioMed Central
Publisher DOI
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Abstract
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 ...
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.
Biosciences - old structure
Collections of Former Colleges
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