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A Study of Network-based Kernel Methods on Protein-Protein Interaction for Protein Functions Prediction

机译：基于网络的蛋白质-蛋白质相互作用预测蛋白质功能的核方法研究

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Predicting protein functions is an important issue in the post-genomic era. In this paper, we studied several network-based kernels including Local Linear Embedding (LLE) kernel method, Diffusion kernel and Laplacian Kernel to uncover the relationship between proteins functions and Protein-Protein Interactions (PPI). We first construct kernels based on PPI networks, we then apply support Vector Machine (SVM) techniques to classify proteins into different functional groups. 5fold cross validation is then applied to the selected 359 GO terms to compare the performance of different kernels and guilt-by-association methods including neighbor counting methods and Chisquare methods. Finally we made predictions of functions of some unknown genes and verified the preciseness of our prediction in part by the information of other data source.

机译：预测蛋白质功能是后基因组时代的重要问题。在本文中，我们研究了几种基于网络的内核，包括局部线性嵌入（LLE）内核方法，扩散内核和拉普拉斯内核，以揭示蛋白质功能与蛋白质-蛋白质相互作用（PPI）之间的关系。我们首先基于PPI网络构建内核，然后应用支持向量机（SVM）技术将蛋白质分为不同的功能组。然后对选定的359个GO项应用5倍交叉验证，以比较不同内核的性能以及包括邻居计数法和Chisquare方法的内关联法的性能。最后，我们对一些未知基因的功能进行了预测，并部分地通过其他数据源的信息验证了我们预测的准确性。

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《Optimization and systems biology》|2009年|p.25-32|共8页
会议地点 Zhangjiajie(CN);Zhangjiajie(CN)
作者
Wai-Ki Ching; Limin Li; Yat-Ming Chan; Hiroshi Mamitsuka;
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作者单位

Advanced Modeling and Applied Computing Laboratory,Department of Mathematics,The University of Hong Kong,Hong Kong;

Advanced Modeling and Applied Computing Laboratory,Department of Mathematics,The University of Hong Kong,Hong Kong;

Advanced Modeling and Applied Computing Laboratory,Department of Mathematics,The University of Hong Kong,Hong Kong;

Bioinformatics Center,Institute for Chemical Research,Kyoto University,Gokasho,Uji,Kyoto 611-0011,Japan;

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会议组织
原文格式 PDF
正文语种 eng
中图分类生物工程学（生物技术）;
关键词
Protein Function Prediction; Kernel Method; Local Linear Embedding (LLE) Kernel; Laplacian Kernel; Diffusion Kernel; Support Vector Machine;

机译：蛋白质功能预测；内核方法；局部线性嵌入（LLE）内核;拉普拉斯内核;扩散内核;支持向量机;

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专利

1. 蛋白质相互作用的分析:利用酵母两性杂交系统探索蛋白质功能 [J] . 中国林学（英文版） . 2002,第001期
2. ON NETWORK-BASED KERNEL METHODS FOR PROTEIN-PROTEIN INTERACTIONS WITH APPLICATIONS IN PROTEIN FUNCTIONS PREDICTION [J] . Limin LI, Waiki CHING, Yatming CHAN, 系统科学与复杂性：英文版 . 2010,第005期

机译：蛋白质相互作用的基于网络的核方法及其在蛋白质功能预测中的应用
3. ON NETWORK-BASED KERNEL METHODS FOR PROTEIN-PROTEIN INTERACTIONS WITH APPLICATIONS IN PROTEIN FUNCTIONS PREDICTION [J] . Limin LI, Waiki CHING, Yatming CHAN, 系统科学与复杂性：英文版 . 2010,第005期

机译：蛋白质相互作用的基于网络的核方法及其在蛋白质功能预测中的应用
4. Modified Potential Functions Result in Enhanced Predictions of a Protein Complex by All-Atom Molecular Dynamics Simulations, Confirming a Stepwise Association Process for Native Protein-Protein Interactions [J] . Li Zhen-lu, Buck Matthias Journal of chemical theory and computation: JCTC . 2019,第8期

机译：改性潜在功能导致全原子分子动力学模拟增强了蛋白质复合物的预测，确认了用于天然蛋白质 - 蛋白质相互作用的逐步关联过程
5. A Study of Network-based Kernel Methods on Protein-Protein Interaction for Protein Functions Prediction [C] . Wai-Ki Ching, Limin Li, Yat-Ming Chan, International symposium on optimization and systems biology . 2009

机译：蛋白质蛋白质蛋白质蛋白质蛋白质蛋白质蛋白质蛋白质蛋白质预测的研究
6. Development of structure-based computational methods for prediction and design of protein-protein interactions. [D] . Pierce, Brian Gregory. 2008

机译：蛋白质和蛋白质相互作用的预测和设计的基于结构的计算方法的发展。
7. Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions [O] . Peiying Ruan, Morihiro Hayashida, Osamu Maruyama, -1

机译：使用新功能和核函数从加权蛋白质-蛋白质相互作用网络预测异二聚体蛋白质复合物
8. A study of network-based kernel methods on protein-protein interaction for protein functions prediction [O] . Ching WK, Chan YM, Li L, 2009

机译：基于网络的蛋白质 - 蛋白质相互作用蛋白质功能预测方法的研究

A Study of Network-based Kernel Methods on Protein-Protein Interaction for Protein Functions Prediction

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