Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection.
Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection.
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DOI：
10.1093/nar/gkm848
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发表时间：
2008-03
影响因子：
14.9
通讯作者：
Li T
中科院分区：
文献类型：
--
作者：
Wan J;Kang S;Tang C;Yan J;Ren Y;Liu J;Gao X;Banerjee A;Ellis LB;Li T
元预测器通过组织和处理由定义的问题域中的几个其他预测器产生的预测来进行预测。一个熟练的元预测器不仅提供了更好的预测性能比单个预测器，它是从构建，但它也减轻了实验研究人员从作出艰难的判断时，面对多个预测程序作出的相互矛盾的结果。随着越来越多的预测程序在生命科学的许多领域中被开发，迫切需要研究有效的元预测策略。我们编译了四个无偏磷酸化位点数据集，每个数据集都是四个主要的丝氨酸/苏氨酸（S/T）蛋白激酶家族之一-CDK，CK 2，PKA和PKC。使用这些数据集，我们研究了几种元预测策略，其中15个磷酸化位点预测因子来自六个预测程序：GPS，KinasePhos，NetPhosK，PPSP，PredPhospho和Scansite。通过限制性网格搜索确定参数的广义加权投票元预测策略构建的元预测器具有最佳性能，在预测所有四个激酶家族的磷酸化位点方面超过了所有单个预测器。我们的研究结果证明了一个有用的决策工具，用于分析各种S/T磷酸化位点预测的预测。这些元预测器的实现可在web上获得：http://MetaPred.umn.edu/MetaPredPS/。
Meta-predictors make predictions by organizing and processing the predictions produced by several other predictors in a defined problem domain. A proficient meta-predictor not only offers better predicting performance than the individual predictors from which it is constructed, but it also relieves experimentally researchers from making difficult judgments when faced with conflicting results made by multiple prediction programs. As increasing numbers of predicting programs are being developed in a large number of fields of life sciences, there is an urgent need for effective meta-prediction strategies to be investigated. We compiled four unbiased phosphorylation site datasets, each for one of the four major serine/threonine (S/T) protein kinase families—CDK, CK2, PKA and PKC. Using these datasets, we examined several meta-predicting strategies with 15 phosphorylation site predictors from six predicting programs: GPS, KinasePhos, NetPhosK, PPSP, PredPhospho and Scansite. Meta-predictors constructed with a generalized weighted voting meta-predicting strategy with parameters determined by restricted grid search possess the best performance, exceeding that of all individual predictors in predicting phosphorylation sites of all four kinase families. Our results demonstrate a useful decision-making tool for analysing the predictions of the various S/T phosphorylation site predictors. An implementation of these meta-predictors is available on the web at: http://MetaPred.umn.edu/MetaPredPS/.
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