Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy.
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.
addresses: School of Biosciences, University of Exeter, Exeter EX4 5DE, UK. firstname.lastname@example.org
notes: PMCID: PMC2777180
types: Journal Article; Research Support, Non-U.S. Gov't
© 2009 Yang; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
BMC Bioinformatics, 2009, Vol. 10, pp. 361
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