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SEQUENCE-BASED PROTEIN-PROTEIN INTERACTION PREDICTION VIA SUPPORT VECTOR MACHINE

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Journal of Systems Science & Complexity 2010年第5期23卷 1012-1023页

作者： Yongcui WANG Jiguang WANG Zhixia YANG naiyang deng College of Science China Agricultural University Beijing 100083 China Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau Biology Chinese Academy of Sciences Xining 810008 China Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China College of Mathematics and Systems Science Xinjiang University Urumuchi 830046 China College of Science China Agricultural University Beijing 100083 China

This paper develops sequence-based methods for identifying novel protein-protein interactions （PPIs） by means of support vector machines （SVMs）. The authors encode proteins ont only in the gene level but also in the amino acid level, and design a procedure to select negative training set for dealing with the training dataset imbalance problem, i.e., the number of interacting protein pairs is scarce relative to large scale non-interacting protein pairs. The proposed methods are validated on PPIs data of Plasmodium falciparum and Escherichia coli, and yields the predictive accuracy of 93.8% and 95.3%, respectively. The functional annotation analysis and database search indicate that our novel predictions are worthy of future experimental validation. The new methods will be useful supplementary tools for the future proteomics studies.

关键词： Imbalance problem protein-protein interactions sequence-based support vector machine.

NEW ROBUST UNSUPERVISED SUPPORT VECTOR MACHINES

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Journal of Systems Science & Complexity 2011年第3期24卷 466-476页

作者： Kun ZHAO Mingyu ZHANG ~ naiyang deng Logistics School Beijing Wuzi University Beijing 101149 China. School of Economic and Management Beijing Jiaotong University Beijing 100044 China. College of Science China Agricultural University Bcijing 100083 China.

This paper proposes robust version to unsupervised classification algorithm based on modified robust version of primal problem of standard SVMs, which directly relaxes it with label variables to a semi-definite progra... 详细信息

关键词： Robust semi-definite programming support vector machines unsupervised learning

A theoretical analysis on efficiency of some Newton-PCG methods

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Science China Mathematics 2005年第8期48卷 1046-1064页

作者： deng naiyang, ZHANG Jianzhong & ZHONG Ping China Agricultural University, Beijing 100083, China City University of Hong Kong, Hong Kong, China China Agricultural University Beijing China City University of Hong Kong Hong Kong China

In this paper, we study the efficiency issue of inexact Newton-type methods for smooth unconstrained optimization problems under standard assumptions from theoretical point of view by discussing a concrete Newton-PCG algorithm. In order to compare the algorithm with Newton's method, a ratio between the measures of their approximate efficiencies is investigated. Under mild conditions, it is shown that first, this ratio is larger than 1, which implies that the Newton-PCG algorithm is more efficient than Newton's method, and second, this ratio increases when the dimension n of the problem increases and tends to infinity at least at a rate In n/In 2 when n→∞, which implies that in theory the Newton-PCG algorithm is much more efficient for middle- and large-scale problems. These theoretical results are also supported by our preliminary numerical experiments.

关键词： unconstrained optimization, Newton's method, preconditioned conjugate gradient method, efficiency.

ON　THE　CONVERGENCE　OF　CONJUGATE　GRADIENT　METHODS INVARIANT TO　NONLINEAR　SCALING

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Systems Science and Mathematical Sciences 1996年第2期9卷 128-133页

作者： deng naiyang LI Zhengfeng (Division of Basic Science, Beijing Agricultural Engineering University, Beijing 100083, China) 中国农业工程大学基础科学部北京

ＯＮＴＨＥＣＯＮＶＥＲＧＥＮＣＥＯＦＣＯＮＪＵＧＡＴＥＧＲＡＤＩＥＮＴＭＥＴＨＯＤＳＩＮＶＡＲＩＡＮＴＴＯＮＯＮＬＩＮＥＡＲＳＣＡＬＩＮＧＤＥＮＧＮａｉｙａｎｇ；ＬＩＺｈｅｎｇｆｅｎｇ（ＤｉｖｉｓｉｏｎｏｆＢａｓｉｃ... 详细信息

ＯＮＴＨＥＣＯＮＶＥＲＧＥＮＣＥＯＦＣＯＮＪＵＧＡＴＥＧＲＡＤＩＥＮＴＭＥＴＨＯＤＳＩＮＶＡＲＩＡＮＴＴＯＮＯＮＬＩＮＥＡＲＳＣＡＬＩＮＧＤＥＮＧＮａｉｙａｎｇ；ＬＩＺｈｅｎｇｆｅｎｇ（ＤｉｖｉｓｉｏｎｏｆＢａｓｉｃＳｃｉｅｎｃｅ，ＢｅｉｊｉｎｇＡｇ...

关键词： GRAD

A NEW CONJUGATE GRADIENT METHOD AND ITS GLOBAL CONVERGENCE PROPERTIES

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Systems Science and Mathematical Sciences 1998年第1期11卷 53-60页

作者： LI Zhengfeng CHEN Jing deng naiyang(Division of Basic Sciences, China Agricultural University East Campus, Beijing 100083, China) 中国农业大学基础科学部北京

This paper presents a new conjugate gradient method for unconstrained opti-mization. This method reduces to the Polak-Ribiere-Polyak method when line searches areexact. But their performances are differellt in the case of inexact line search. By a simpleexample, we show that the Wolf e conditions do not ensure that the present method and thePolak- Ribiere- Polyak method will pro duce descent direct i0ns even u nder t h e ass umpt ionthat the objective function is Strictly convex. This result contradicts the F0lk axiom thatthe Polak-Ribiere-Polyak with the Wolf e line search should find the minimizer of a strictlyconvex objective function. Finally, we show that there are two ways to improve the newmethod such that it is globally convergent.

关键词： Conjugate gradient method global convergence unconstrained optimization line searches.

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模糊支持向量分类机

计算机工程 2005年第20期31卷 25-26,32页

作者：杨志民田英杰邓乃扬中国农业大学经济管理学院中国农业大学理学院北京100083

研究了当训练点的输出为模糊数时支持向量分类机的构建问题。对于线性模糊分类问题,首先将其转化为模糊系数规划。利用模糊系数规划的λ?最优规划,求解模糊系数规划得到模糊最优解(模糊集合)以及模糊最优分类函数集(取值为最优分类函数... 详细信息

研究了当训练点的输出为模糊数时支持向量分类机的构建问题。对于线性模糊分类问题,首先将其转化为模糊系数规划。利用模糊系数规划的λ?最优规划,求解模糊系数规划得到模糊最优解(模糊集合)以及模糊最优分类函数集(取值为最优分类函数而隶属度为λ(0≤λ≤1)的模糊集合),从而构造线性模糊支持向量分类机。对于非线性模糊分类问题,引入核函数,类似于线性模糊分类问题得到非线性模糊支持向量分类机。最后构造显示模糊支持向量分类机特点的模糊支持向量集(取值为模糊训练点,隶属度为λ(0≤λ≤1)的模糊集合)。模糊支持向量分类机较好地解决了支持向量机中含有模糊信息的分类问题。

关键词：支持向量机模糊数模糊系数规划模糊支持向量分类机

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一种改进的支持向量分类方法及其应用

计算机工程 2005年第8期31卷 153-154页

作者：沈翠华刘广利邓乃扬中国农业大学信息与电气工程学院北京100083

提出了一种改进的支持向量分类方法——NN-SVM-KNN。用最近邻((NN)对训练集进行修剪,用支持向量机(SVM)进行分类,同时在分类过程中,判断待识样本是否落在间隔之外,若是直接用SVM分类,否则,用所有的支持向量作为训练集,进行K近邻分类。... 详细信息

提出了一种改进的支持向量分类方法——NN-SVM-KNN。用最近邻((NN)对训练集进行修剪,用支持向量机(SVM)进行分类,同时在分类过程中,判断待识样本是否落在间隔之外,若是直接用SVM分类,否则,用所有的支持向量作为训练集,进行K近邻分类。并把这种方法应用到个人信用评估中,和其它信用评估方法比较,得到了比较好的结果。

关键词：支持向量机信用评估最近邻

线性支持向量顺序回归机的原始问题的解集分析

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运筹学学报 2007年第3期11卷 105-112页

作者：杨志霞邓乃扬中国农业大学理学院

本文主要对线性支持向量顺序回归机进行理论研究.对其相应原始问题解的存在性唯一性问题进行细致的分析,指明其解集的确切结构,并给出由对偶问题的解求出原始问题的解集的具体步骤.从而为建立理论上完备的线性支持向量顺序回归机提供了... 详细信息

本文主要对线性支持向量顺序回归机进行理论研究.对其相应原始问题解的存在性唯一性问题进行细致的分析,指明其解集的确切结构,并给出由对偶问题的解求出原始问题的解集的具体步骤.从而为建立理论上完备的线性支持向量顺序回归机提供了依据.

关键词：运筹学非线性最优化顺序回归线性支持向量顺序回归机

The new interpretation of support vector machines on statistical learning theory

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Science China Mathematics 2010年第1期53卷 151-164页

作者： ZHANG ChunHua 1 , TIAN YingJie 2 & deng naiyang 3,1 School of Information, Renmin University of China, Beijing 100872, China 2 Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100080, China 3 College of Science, China Agricultural University, Beijing 100083, China School of Information Renmin University of China Beijing China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China College of Science China Agricultural University Beijing China

This paper is concerned with the theoretical foundation of support vector machines (SVMs). The purpose is to develop further an exact relationship between SVMs and the statistical learning theory (SLT). As a representative, the standard C-support vector classification (C-SVC) is considered here. More precisely, we show that the decision function obtained by C-SVC is just one of the decision functions obtained by solving the optimization problem derived directly from the structural risk minimization principle. In addition, an interesting meaning of the parameter C in C-SVC is given by showing that C corresponds to the size of the decision function candidate set in the structural risk minimization principle.

关键词： C-support vector classification the minimization principle of the structural risk KKT conditions