DETECTING AND SORTING TARGETING PEPTIDES WITH NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

JOHN HAWKINS; MIKAEL BODéN

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DETECTING AND SORTING TARGETING PEPTIDES WITH NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

机译：用神经网络和支持向量机检测和排序目标肽

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This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

机译：本文提出了一种复合多层分类器系统，用于基于蛋白质的氨基酸序列预测蛋白质的亚细胞定位。这项工作是对我们先前的预测器PProwler v1.1的扩展，它本身是基于一系列预测器SignalP和TargetP构建的。在这项研究中，我们概述了为改进分类器设计而进行的实验。主要的改进来自将Support Vector机器用作“智能门”，对几种不同的靶向肽检测网络的输出进行了分类。我们的最终模型（PProwler v1.2）给出的非植物MCC值为0.873，植物蛋白的MCC值为0.849。对于植物数据，该模型将我们先前的亚细胞定位预测因子（PProwler v1.1）的准确性提高了2％（表示比TargetP提高了7.5％）。

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《Journal of Bioinformatics and Computational Biology》 |2006年第1期|共18页
作者
JOHN HAWKINS; MIKAEL BODéN;
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正文语种 eng
中图分类细胞生物学;
关键词
Protein subcellular localization; machine learning; neural network; recurrent neural network; Support Vector machine; targeting peptide; amino acid sequence;

机译：蛋白质亚细胞定位;机器学习;神经网络;递归神经网络;支持向量机;靶向肽;氨基酸序列;

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DETECTING AND SORTING TARGETING PEPTIDES WITH NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

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