Subcellular localization prediction of the proteome is one of major goals of large-scale genome or proteome sequencing projects to define the gene functions that could be possible with the help of computational modeling techniques. Previously, different methods have been developed for this purpose using multi-label classification system and achieved a high level of accuracy. However, during the validation of our blind dataset of plant vacuole proteins, we observed that they have poor performance with accuracy value range from ~1.3% to 48.5%. The results showed that the previously developed methods are not very accurate for the plant vacuole protein prediction and thus emphasize the need to develop a more accurate and reliable algorithm. In this study, we have developed various compositions as well as PSSM-based models and achieved a high accuracy than previously developed methods. We have shown that our best model achieved ~63% accuracy on blind dataset, which is far better than currently available tools. Furthermore, we have implemented our best models in the form of GUI-based free software called ‘VacPred’ which is compatible with both Linux and Window platform. This software is freely available for download at www.deepaklab.com/vacpred .

中文翻译：

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VacPred：使用机器学习技术对植物液泡蛋白进行基于序列的预测

蛋白质组的亚细胞定位预测是大规模基因组或蛋白质组测序项目的主要目标之一，以定义在计算建模技术的帮助下可能实现的基因功能。以前，为此目的使用多标签分类系统开发了不同的方法，并实现了高水平的准确性。然而，在验证我们的植物液泡蛋白盲数据集时，我们观察到它们的性能很差，准确度值范围从~1.3% 到 48.5%。结果表明，先前开发的方法对于植物液泡蛋白质预测不是很准确，因此强调需要开发更准确和可靠的算法。在这项研究中，我们开发了各种组合以及基于 PSSM 的模型，并实现了比以前开发的方法更高的准确性。我们已经证明我们最好的模型在盲数据集上达到了约 63% 的准确率，这远远优于当前可用的工具。此外，我们以基于 GUI 的免费软件“VacPred”的形式实现了我们最好的模型，该软件与 Linux 和 Window 平台兼容。该软件可在 www.deepaklab.com/vacpred 上免费下载。