Meta-prediction of protein subcellular localization with reduced voting.
Meta-prediction of protein subcellular localization with reduced voting.
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DOI：
10.1093/nar/gkm562
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发表时间：
2007
影响因子：
14.9
通讯作者：
Li, Tongbin
中科院分区：
文献类型：
--
作者：
Liu, Jie;Kang, Shuli;Tang, Chuanning;Ellis, Lynda B M;Li, Tongbin
元预测试图利用多个预测程序的组合优势，希望实现超过定义问题域中所有现有预测者的预测性能。我们研究了四室真核亚细胞定位问题的元预测问题。我们汇编了来自Swiss-Prot 50.2的1693个核、细胞质、线粒体和细胞外动物蛋白的无偏亚细胞定位数据集。使用这个数据集，我们评估了来自八个独立的亚细胞定位预测程序的12个预测因子的预测性能：ELSPred、LOCtree、PLOC、蛋白质组分析、PSORT、PSORT II、SubLoc和Wolf PSORT。戈罗德金相关系数(GCC)是衡量成绩的指标之一。蛋白质组分析员是在这个四室预测问题中测试的最好的个体亚细胞定位预测者，GCC=0.811。剔除12个预测因子中的6个的简化投票策略产生一个元预测因子(RAW-RAG-6)，GCC=0.856，显著优于所有测试的单个亚细胞定位预测因子(P=8.2x10−6，Fisher‘s Z变换检验)。当元预测器使用开发中未使用的数据进行测试时，性能的改进将持续存在。这种投票策略和类似的投票策略，如果应用得当，有望产生在其他生命科学问题领域表现突出的元预测因子。
Meta-prediction seeks to harness the combined strengths of multiple predicting programs with the hope of achieving predicting performance surpassing that of all existing predictors in a defined problem domain. We investigated meta-prediction for the four-compartment eukaryotic subcellular localization problem. We compiled an unbiased subcellular localization dataset of 1693 nuclear, cytoplasmic, mitochondrial and extracellular animal proteins from Swiss-Prot 50.2. Using this dataset, we assessed the predicting performance of 12 predictors from eight independent subcellular localization predicting programs: ELSPred, LOCtree, PLOC, Proteome Analyst, PSORT, PSORT II, SubLoc and WoLF PSORT. Gorodkin correlation coefficient (GCC) was one of the performance measures. Proteome Analyst is the best individual subcellular localization predictor tested in this four-compartment prediction problem, with GCC = 0.811. A reduced voting strategy eliminating six of the 12 predictors yields a meta-predictor (RAW-RAG-6) with GCC = 0.856, substantially better than all tested individual subcellular localization predictors (P = 8.2 × 10−6, Fisher's Z-transformation test). The improvement in performance persists when the meta-predictor is tested with data not used in its development. This and similar voting strategies, when properly applied, are expected to produce meta-predictors with outstanding performance in other life sciences problem domains.