Assfalg Johannes, Gong Jing, Kriegel Hans-Peter, Pryakhin Alexey, Wei Tiandi, Zimek Arthur
Institute for Informatics, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, 80538 Munich, Germany.
J Bioinform Comput Biol. 2009 Apr;7(2):269-85. doi: 10.1142/s0219720009004072.
In the past decade, many automated prediction methods for the subcellular localization of proteins have been proposed, utilizing a wide range of principles and learning approaches. Based on an experimental evaluation of different methods and their theoretical properties, we propose to combine a well-balanced set of existing approaches to new, ensemble-based prediction methods. The experimental evaluation shows that our ensembles improve substantially over the underlying base methods.
在过去十年中，已经提出了许多用于蛋白质亚细胞定位的自动预测方法，这些方法利用了广泛的原理和学习方法。基于对不同方法及其理论特性的实验评估，我们建议将一组均衡的现有方法组合成新的基于集成的预测方法。实验评估表明，我们的集成方法比基础方法有显著改进。
