基于支持向量机的蛋白质同源寡聚体分类研究

基于支持向量机和贝叶斯方法,从蛋白质一级序列出发对蛋白质同源二聚体、同源三聚体、同源四聚体、同源六聚体进行分类研究,结果表明：基于支持向量机, 采用“一对多”和“一对一”策略, 其分类总精度分别为77.36%和93.43%, 分别比基于贝叶斯协方差判别法的分类总精度50.64%提高26.72和42.79个百分点.从而说明支持向量机可用于蛋白质同源寡聚体分类,且是一种非常有效的方法.对于多类蛋白质同源寡聚体分类,基于相同的机器学习方法（如支持向量机）,采用“一对一”策略比“一对多”效果好.同时亦表明蛋白质同源寡聚体一级序列包含四级结构信息.     

用支持向量机识别 β-发夹模体

基于蛋白质序列,提出了一种新的超二级结构模体β-发夹的预测方法。利用离散增量构成的向量来表示序列信息,并将6个离散增量输入支持向量机,在六维向量空间中寻找最优超平面,将β-发夹和非β-发夹进行分类。计算结果表明,利用所设计的算法预测β-发夹,有较高的预测能力。对于训练集,5-交叉检验的预测总精度为81.24％,相关系数为0．57,β-发夹敏感性为83．06％;对于独立的检验集,预测总精度为78．34％,相关系数0．56,β-发夹敏感性为77．24％。将此预测模型应用于CASP6的63个蛋白质进行检验,得到较好结果。     

基于支持向量机方法的蛋白可溶性预测

按照蛋白质序列中残基的相对可溶性,将其分为两类(表面／内部)和三类(表面／中间／内部)进行预测。选择不同窗宽和参数对数据进行训练和预测,以确保得到最好的分类效果,并同其他已有方法进行比较。对同一数据集不同分类阈值的预测结果显示,支持向量机方法对蛋白质可溶性的整体预测效果优于神经网络和信息论的方法。其中,对两类数据的最优分类结果达到79．0％,对三类数据的最优分类结果达到67．5％,表明支持向量机是蛋白质残基可溶性预测的一种有效方法。     

支持向量机及组合预测在蛋白质四级结构分类中的应用

目的:基于支持向量机建立一个自动化识别新肽链四级结构的方法,提高现有方法的识别精度.方法:改进4种已有的蛋白质一级序列特征值提取方法,采用线性和非线性组合预测方法建立一个有效的组合预测模型.结果:以同源二聚体及非同源二聚体为例.对4种特征值提取方法进行改进后其分类精度均提升了2～3%;进一步实施线性与非线性组合预测后,其分类精度再次提高了2～3%,使独立测试集的分类精度达到了90%以上.结论:4种特征值提取方法均较好地反应出蛋白质一级序列包含四级结构信息,组合预测方法能有效地集多种特征值提取方法优势于一体.     

结合支持向量机和贝叶斯方法进行蛋白质二级结构预测

组建一个分两个阶段的分类器来进行蛋白质二级结构预测。第一阶段由支持向量机分类器组成,在第二阶段中使用第一阶段已预测的结果来进行贝叶斯判别。预测性能的改进表明了结合支持向量机和贝叶斯方法预测性能优越于单独使用支持向量机的预测性能。同时也证明残基在形成二级结构时是相互影响的。     

基于添加模体信息和功率谱密度的组合向量预测27类蛋白质折叠子

以序列相似性低于40%的1895条蛋白质序列构建涵盖27个折叠类型的蛋白质折叠子数据库,从蛋白质序列出发,用模体频数值、低频功率谱密度值、氨基酸组分、预测的二级结构信息和自相关函数值构成组合向量表示蛋白质序列信息,采用支持向量机算法,基于整体分类策略,对27类蛋白质折叠子的折叠类型进行预测,独立检验的预测精度达到了66.67%。同时,以同样的特征参数和算法对27类折叠子的4个结构类型进行了预测,独立检验的预测精度达到了89.24%。将同样的方法用于前人使用过的27类折叠子数据库,得到了好于前人的预测结果。     

异源蛋白质相互作用数据整合算法的进展

蛋白质相互作用在生物学过程和细胞功能行使中起核心作用。高通量技术的应用结合计算机预测方法的发展,使得直接和间接来源的蛋白质相互作用数据得到了大规模的增加。如何系统地整合这些数据并从中提取有用的信息是一项挑战,这也促使了许多整合算法应运而生。本文综述了八种整合蛋白质相互作用数据源的方法：投票、支持向量机、朴素贝叶斯、逻辑斯蒂回归、决策树、随机森林、基于随机森林的k-近邻法以及混合属性分类等方法。     

基于氨基酸组成分布的蛋白质同源寡聚体分类研究

基于一种新的特征提取方法——氨基酸组成分布,使用支持向量机作为成员分类器,采用“一对一”的多类分类策略,从蛋白质一级序列对四类同源寡聚体进行分类研究。结果表明,在10-CV检验下,基于氨基酸组成分布,其总分类精度和精度指数分别达到了86.22%和67.12%,比基于氨基酸组成成分的传统特征提取方法分别提高了5.74和10.03个百分点,比二肽组成成分特征提取方法分别提高了3.12和5.63个百分点,说明氨基酸组成分布对于蛋白质同源寡聚体分类是一种非常有效的特征提取方法;将氨基酸组成分布和蛋白质序列长度特征组合,其总分类精度和精度指数分别达到了86.35%和67.23%,说明蛋白质序列长度特征含有一定的空间结构信息。     

支持向量机方法预测蛋白质结构中的二硫键

在蛋白质结构预测的研究中,一个重要的问题就是正确预测二硫键的连接,二硫键的准确预测可以减少蛋白质构像的搜索空间,有利于蛋白质3D结构的预测,本文将预测二硫键的连接问题转化成对连接模式的分类问题,并成功地将支持向量机方法引入到预测工作中。通过对半胱氨酸局域序列连接模式的分类预测,可以由蛋白质的一级结构序列预测该蛋白质的二硫键的连接。结果表明蛋白质的二硫键的连接与半胱氨酸局域序列连接模式有重要联系,应用支持向量机方法对蛋白质结构的二硫键预测取得了良好的结果。     

基于支持向量机融合网络的蛋白质折叠子识别研究

在不依赖于序列相似性的条件下,蛋白质折叠子识别是一种分析蛋白质结构的重要方法.提出了一种三层支持向量机融合网络,从蛋白质的氨基酸序列出发,对27类折叠子进行识别.融合网络使用支持向量机作为成员分类器,采用“多对多”的多类分类策略,将折叠子的6种特征分为主要特征和次要特征,构建了多个差异的融合方案,然后对这些融合方案进行动态选择得到最终决策.当分类之前难以确定哪些参与组合的特征种类能够使分类结果最好时,提供了一种可靠的解决方案来自动选择特征信息互补最大的组合,保证了最佳分类结果.最后,识别系统对独立测试样本的总分类精度达到61.04%.结果和对比表明,此方法是一种有效的折叠子识别方法.     

基于序列特征的人类PolⅡ启动子理论预测

基于已知的人类PolII启动子序列数据,综合选取启动子序列内容和序列信号特征,构建启动子的支持向量机分类器．分别以启动子序列的6-mer频数作为离散源参数构建序列内容特征。同时选取24个位点的3-mer频数作为序列信号特征构建PWM,将所得到的两类参数输入支持向量机对人类启动子进行预测．用10折叠交叉检验和独立数据集来衡量算法的预测能力,相关系数指标达到95％以上,结果显示结合了支持向量机的离散增量算法能够有效的提高预测成功率,是进行真核生物启动子预测的一种很有效的方法．     

An SVM method using evolutionary information for the identification of allergenic proteins

This study presents an allergenic protein prediction system that appears to be capable of producing high sensitivity and specificity. The proposed system is based on support vector machine (SVM) using evolutionary information in the form of an amino acid position specific scoring matrix (PSSM). The performance of this system is assessed by a 10-fold cross-validation experiment using a dataset consisting of 693 allergens and 1041 non-allergens obtained from Swiss-Prot and Structural Database of Allergenic Proteins (SDAP). The PSSM method produced an accuracy of 90.1% in comparison to the methods based on SVM using amino acid, dipeptide composition, pseudo (5-tier) amino acid composition that achieved an accuracy of 86.3, 86.5 and 82.1% respectively. The results show that evolutionary information can be useful to build more effective and efficient allergen prediction systems.     

基于分段氨基酸组成成分的蛋白质相互作用预测

蛋白质相互作用研究有助于揭示生命过程的许多本质问题,也有助于疾病预防、诊断,对药物研制具有重要的参考价值。文章首先构建出蛋白质作用数据库,提出分段氨基酸组成成分特征提取方法来预测蛋白质相互作用。10CV检验下,基于支持向量机的3段氨基酸组成成分特征提取方法的预测总精度为86.2%,比传统的氨基酸组成成分方法提高2.31个百分点;采用Guo的数据库和检验方法,3段氨基酸组成成分特征提取方法的预测总精度为90.11%,比Guo的自相关函数特征提取方法提高2.75个百分点,从而表明分段氨基酸组成成分特征提取方法可有效地应用于蛋白质相互作用预测。     

Combing ontologies and dipeptide composition for predicting DNA-binding proteins

Given a novel protein it is very important to know if it is a DNA-binding protein, because DNA-binding proteins participate in the fundamental role to regulate gene expression. In this work, we propose a parallel fusion between a classifier trained using the features extracted from the gene ontology database and a classifier trained using the dipeptide composition of the protein. As classifiers the support vector machine (SVM) and the 1-nearest neighbour are used. Matthews's correlation coefficient obtained by our fusion method is approximately 0.97 when the jackknife cross-validation is used; this result outperforms the best performance obtained in the literature (0.924) using the same dataset where the SVM is trained using only the Chou's pseudo amino acid based features. In this work also the area under the ROC-curve (AUC) is reported and our results show that the fusion permits to obtain a very interesting 0.995 AUC. In particular we want to stress that our fusion obtains a 5% false negative with a 0% of false positive. Matthews's correlation coefficient obtained using the single best GO-number is only 0.7211 and hence it is not possible to use the gene ontology database as a simple lookup table. Finally, we test the complementarity of the two tested feature extraction methods using the Q-statistic. We obtain the very interesting result of 0.58, which means that the features extracted from the gene ontology database and the features extracted from the amino acid sequence are partially independent and that their parallel fusion should be studied more.     

Prediction of thermophilic proteins using feature selection technique

The thermostability of proteins is particularly relevant for enzyme engineering. Developing a computational method to identify mesophilic proteins would be helpful for protein engineering and design. In this work, we developed support vector machine based method to predict thermophilic proteins using the information of amino acid distribution and selected amino acid pairs. A reliable benchmark dataset including 915 thermophilic proteins and 793 non-thermophilic proteins was constructed for training and testing the proposed models. Results showed that 93.8% thermophilic proteins and 92.7% non-thermophilic proteins could be correctly predicted by using jackknife cross-validation. High predictive successful rate exhibits that this model can be applied for designing stable proteins.     

PSoL: a positive sample only learning algorithm for finding non-coding RNA genes

MOTIVATION: Small non-coding RNA (ncRNA) genes play important regulatory roles in a variety of cellular processes. However, detection of ncRNA genes is a great challenge to both experimental and computational approaches. In this study, we describe a new approach called positive sample only learning (PSoL) to predict ncRNA genes in the Escherichia coli genome. Although PSoL is a machine learning method for classification, it requires no negative training data, which, in general, is hard to define properly and affects the performance of machine learning dramatically. In addition, using the support vector machine (SVM) as the core learning algorithm, PSoL can integrate many different kinds of information to improve the accuracy of prediction. Besides the application of PSoL for predicting ncRNAs, PSoL is applicable to many other bioinformatics problems as well. RESULTS: The PSoL method is assessed by 5-fold cross-validation experiments which show that PSoL can achieve about 80% accuracy in recovery of known ncRNAs. We compared PSoL predictions with five previously published results. The PSoL method has the highest percentage of predictions overlapping with those from other methods.     

Predicting protein solubility with a hybrid approach by pseudo amino acid composition

Protein solubility plays a major role for understanding the crystal growth and crystallization process of protein. How to predict the propensity of a protein to be soluble or to form inclusion body is a long but not fairly resolved problem. After choosing almost 10,000 protein sequences from NCBI database and eliminating the sequences with 90% homologous similarity by CD-HIT, 5692 sequences remained. By using Chou's pseudo amino acid composition features, we predict the soluble protein with the three methods: support vector machine (SVM), back propagation neural network (BP Neural Network) and hybrid method based on SVM and BP Neural Network, respectively. Each method is evaluated by re-substitution test and 10-fold cross-validation test. In the re-substitution test, the BP Neural Network performs with the best results, in which the accuracy achieves 0.9288 and Matthews Correlation Coefficient (MCC) achieves 0.8513. Meanwhile, the other two methods are better than BP Neural Network in 10-fold cross-validation test. The hybrid method based on SVM and BP Neural Network is the best. The average accuracy is 0.8678 and average MCC is 0.7233. Although all of the three methods achieve considerable evaluations, the hybrid method is deemed to be the best, according to the performance comparison.     

基于离散增量结合支持向量机方法的凋亡蛋白亚细胞位置预测

根据凋亡蛋白的亚细胞位置主要决定于它的氨基酸序列这一观点,基于局部氨基酸序列的n肽组分和序列的亲疏水性分布信息,采用离散增量结合支持向量机(ID_SVM)算法,对六类细胞凋亡蛋白的亚细胞位置进行预测。结果表明,在Re-substitution检验和Jackknife检验下,ID_SVM算法的总体预测成功率分别达到了94.6%和84.2%;在5-fold检验和10-fold检验下,其总体预测成功率也都达到了83%以上。通过比较ID和ID_SVM两种方法的预测能力发现,结合了支持向量机的离散增量算法能够改进预测成功率,结果表明ID_SVM是预测凋亡蛋白亚细胞位置的一种很有效的方法。     

Prediction of protein homo-oligomer types by pseudo amino acid composition: Approached with an improved feature extraction and Naive Bayes Feature Fusion

Summary. The interaction of non-covalently bound monomeric protein subunits forms oligomers. The oligomeric proteins are superior to the monomers within the scope of functional evolution of biomacromolecules. Such complexes are involved in various biological processes, and play an important role. It is highly desirable to predict oligomer types automatically from their sequence. Here, based on the concept of pseudo amino acid composition, an improved feature extraction method of weighted auto-correlation function of amino acid residue index and Naive Bayes multi-feature fusion algorithm is proposed and applied to predict protein homo-oligomer types. We used the support vector machine (SVM) as base classifiers, in order to obtain better results. For example, the total accuracies of A, B, C, D and E sets based on this improved feature extraction method are 77.63, 77.16, 76.46, 76.70 and 75.06% respectively in the jackknife test, which are 6.39, 5.92, 5.22, 5.46 and 3.82% higher than that of G set based on conventional amino acid composition method with the same SVM. Comparing with Chou’s feature extraction method of incorporating quasi-sequence-order effect, our method can increase the total accuracy at a level of 3.51 to 1.01%. The total accuracy improves from 79.66 to 80.83% by using the Naive Bayes Feature Fusion algorithm. These results show: 1) The improved feature extraction method is effective and feasible, and the feature vectors based on this method may contain more protein quaternary structure information and appear to capture essential information about the composition and hydrophobicity of residues in the surface patches that buried in the interfaces of associated subunits; 2) Naive Bayes Feature Fusion algorithm and SVM can be referred as a powerful computational tool for predicting protein homo-oligomer types.