基于有向双关系图和多核融合的蛋白质功能预测

计算机应用 ›› 2014, Vol. 34 ›› Issue (12): 3433-3437.

基于有向双关系图和多核融合的蛋白质功能预测

孟军,刁印

大连理工大学计算机科学与技术学院,辽宁大连 116024

收稿日期:2014-06-23 修回日期:2014-08-11 发布日期:2014-12-31 出版日期:2014-12-01
通讯作者: 孟军
作者简介:孟军(1964-）,女,辽宁大连人,副教授,博士,CCF会员,主要研究方向:机器学习、数据挖掘；刁印(1984-）,男,陕西咸阳人,硕士研究生,主要研究方向:机器学习、数据挖掘。
基金资助:
辽宁省自然科学基金资助项目

Protein function prediction based on directed bi-relational graph and multi-kernel fusion

MENG Jun,DIAO Yin

School of Computer Science and Technology, Dalian University of Technology, Dalian Liaoning 116024, China

Received:2014-06-23 Revised:2014-08-11 Online:2014-12-31 Published:2014-12-01
Contact: MENG Jun
Supported by:
Natural Science Foundation of Liaoning Province of China

摘要/Abstract

摘要：

针对多源异构蛋白质相互作用网络信息量大、数据冗余导致预测结果不能充分反映数据分布信息的问题,将功能类别网络和蛋白质相互作用网络相结合,提出基于有向双关系图和多核融合的多标记学习算法。首先,构建基于含有损失函数的目标方程和最大期望算法的自适应模型；然后,利用图优化策略融合功能类别和蛋白质相互作用网络构成的多个关联矩阵；最后,将融合后的关联矩阵代入模型中预测蛋白质功能。在Yeast和Mouse的蛋白质多源异构数据上的实验结果表明,提出的方法具有预测准确率高、标签损失率低等优势。

Abstract:

In view of the problem that protein interaction network of multiple kernels from heterogeneous data sources contains huge amount of information. Due to data redundancy, the predicted results could not fully reflect the distribution of data. The functional categories network and protein interaction networks were combined, a multi-label learning algorithm was proposed based on the directed bi-relational graph theory and multi-kernel fusion. First, an adaptive learning model was built by the loss function of equation and expectation maximization algorithm. Then, multiple associative matrices were obtained by using the graph optimization strategy to fuse the functional categories and protein interaction networks. Finally, the prediction model was built by the associative matrices and adaptive learning model. The experimental results using multiple heterogeneous protein data sources of Yeast and Mouse show that the proposed method has higher prediction accuracy and lower loss rate of label.

中图分类号:

TP181

孟军刁印. 基于有向双关系图和多核融合的蛋白质功能预测[J]. 计算机应用, 2014, 34(12): 3433-3437.

MENG Jun DIAO Yin. Protein function prediction based on directed bi-relational graph and multi-kernel fusion[J]. Journal of Computer Applications, 2014, 34(12): 3433-3437.

参考文献

［1］LANCKRIET G R G, CRISTIANINI N,JORDAN M I, et al. Kernel-based integration of genomic data using semidefinite programming ［M］. Cambridge: MIT Press, 2003: 231-235.
［2］LANCKRIET G R G, de BIE T, CRISTIANINI N, et al. A statistical framework for genomic data fusion［J］. Bioinformatics, 2004, 20(16): 2626-2635.
［3］TSUDA K, SHIN H, SCHLKOPF B. Fast protein classification with multiple networks［J］. Bioinformatics, 2005, 21(S2): 59-65.
［4］SHIN H, TSUDA K, SCHLKOPF B. Protein functional class prediction with a combined graph［J］. Expert Systems with Applications, 2009, 36(2): 3284-3292.
［5］MOSTAFAVI S, MORRIS Q. Fast integration of heterogeneous data sources for predicting gene function with limited annotation［J］. Bioinformatics, 2010, 26(14): 1759-1765.
［6］BUCAK S S, JIN R, JAIN A K. Multi-label multiple kernel learning by stochastic approximation: Application to visual object recognition［C］// Advances in Neural Information Processing Systems. La Jolla, CA: Neural Information Processing Systems, 2010: 325-333.
［7］YU G, RANGWALA H, DOMENICONI C, et al. Protein function prediction by integrating multiple kernels［C］// Proceedings of the 23rd International Joint Conference on Artificial Intelligence. Menlo Park, California: AAAI Press, 2013: 1869-1875.
［8］JIANG J Q, McQUAY L J. Predicting protein function by multi-label correlated semi-supervised learning［J］. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012, 9(4): 1059-1069.
［9］ZHOU D, BOUSQUET O, LAL T N, et al. Learning with local and global consistency ［C］// Advances in Neural Information Processing Systems. Cambridge: MIT Press,2004: 321-328.
［10］JIANG J Q. Learning protein functions from bi-relational graph of proteins and function annotations ［M］. Berlin: Springer, 2011:128-138.
［11］YU G, DOMENICONI C, RANGWALA H, et al. Transductive multi-label ensemble classification for protein function prediction［C］// Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2012: 1077-1085.
［12］WANG M, HUA X S, YUAN X, et al. Optimizing multi-graph learning: towards a unified video annotation scheme［C］// Proceedings of the 15th ACM International Conference on Multimedia. New York: ACM, 2007: 862-871.
［13］LIAN Y,XIONG H. Multi-kernel learning method for predicting protein secondary structures ［J］. Journal of Computer Applications, 2013,33(S1):43-45.(连云涓,熊惠霖.蛋白质二级结构预测的多核学习方法［J］.计算机应用,2013,33(S1):43-45.
［14］XU J, JAGADEESH V, MANJUNATHB S. Multi-label learning with fused multimodal bi-relational graph［J］. IEEE Transactions on Multimedia, 2014,16(2):403-412.
［15］ZHANG X, DAI D. A framework for incorporating functional interrelationships into protein function prediction algorithms ［J］. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012,9(3): 740–753.
［16］KONG X, NG M, ZHOU Z. Transductive multi-label learning via label set propagation ［J］. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(3): 704-719.
［17］YU G, RANGWALA H, DOMENICONI C, et al. Protein function prediction with incomplete annotations［J］. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2014, 11(3): 579-591.

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