基于集成学习方法的蛋白质相互作用预测

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基于集成学习方法的蛋白质相互作用预测

信息工程 | 更新时间：2023-11-14

- 基于集成学习方法的蛋白质相互作用预测
- Prediction of Protein-protein Interactions Based on Ensemble Learning
- 工程科学与技术 2011年43卷第3期页码：68-75
- 作者机构：
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(20972103;20905054)
- DOI：
  中图分类号：
- 纸质出版日期：2011，
  
  网络出版日期：2011-1-18，
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朱敏,张永清,李梦龙,周大威,黄俊.基于集成学习方法的蛋白质相互作用预测[J].工程科学与技术,2011,43(3):68-75.

Prediction of Protein-protein Interactions Based on Ensemble Learning[J]. Advanced Engineering Sciences, 2011,43(3):68-75.
朱敏,张永清,李梦龙,周大威,黄俊.基于集成学习方法的蛋白质相互作用预测[J].工程科学与技术,2011,43(3):68-75. DOI：

Prediction of Protein-protein Interactions Based on Ensemble Learning[J]. Advanced Engineering Sciences, 2011,43(3):68-75. DOI：

摘要

中文摘要: 针对蛋白质相互作用的预测问题，提出了集成学习的方法。该方法使用人工神经网络和支持向量机为成员分类器的集成学习方法，并分别用自协方差编码方式和二肽组成来表示蛋白质序列的特征集合，预测的准确率和ROC曲线面积分别达到92.16%、94.38%和0.972 5、0.981 5。通过对成员分类器、集成学习方法以及集成学习方法之间的预测效果进行比较，结果表明，集成学习方法可获得更优的预测效果，并能有效提高预测精度

避免采样学习带来的不稳定性。

Abstract

Abstract:To effectively predict the protein-protein interactions

an ensemble learning with artificial neural network and supporting vector machine as the individuals was used

and the feature selection of protein sequence was indicated by auto covariance and dipeptide composition.The prediction accuracy and AUC reached 92.16%

94.38% and 0.972 5

0.981 5

respectively. A comparison among artificial neural network

supporting vector machine and ensemble learning showed that the ensemble learning revealed more superior performance than the others and can improve the prediction accuracy while avoiding the instability of prediction caused by sampling learning.

关键词

集成学习人工神经网络支持向量机蛋白质相互作用

Keywords

ensemble learningartificial neural networksupport vector machineprotein-protein interactions

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