应用QSAR预测人双载蛋白Ⅰ型SH3结构域及其配体多肽的亲和力

蛋白质间的相互作用在信号转导和其他许多生物进程有着至关重要的作用.从20种天然氨基酸的554个物化性质中,单变量建模方法挑选出疏水、静电、立体、氢键4个描述子表征能与人类双载蛋白SH3结构域结合的多肽,预测SH3结构域-肽亲和力.所得描述子并未对变量进行主成分分析(PCA)压缩,且只对氨基酸侧链性质进行结构表征,因此应用这4个描述子并结合多元线性回归(MLR)建模方法对SH3结构域-肽体系进行定量构效关系(OSAR)研究分析域-肽亲和力.测试集的多肽用于模型的验证,内部验证复相关系数(R2)和外部留一法交互验证相关系数(Q2)分别为:0.682,0.650,预测均方根误差(RMSE)为0.528,从模型统计数据可知,QSAR模型预测模型稳定性高,预测能力强.说明这4个描述子物具有更为确切的物化意义,表征过程更加简洁有效且研究效率高的多重优点,并可以拓展预测不同的域-肽体系.     

人脸全局特征识别研究

人脸识别是模式中的一个相当重要却又十分困难的课题。本文利用神经网络（Ｎｅｕｒａｌ Ｎｅｔｗｏｒｋ，简称ＮＮ）及主元分析法（Ｐｒｉｎｃｉｐｌｅ Ｃｏｍｐｏｎｅｎｔ Ａｎａｌｙｓｉｓ），简称ＰＣＡ）不同的特性提出了两种人脸识别的模型：ＮＮ＋ＮＮ模型及ＰＣＡ＋ＮＮ的模型。理论分析和实验结果表示：这两种新的识别模型可以实现优化特征抽取和自适应识别。     

基于荧光卟啉传感器阵列系统的肺癌标志物检测研究

针对肺癌呼出挥发性有机气体（VOCs）中的特定标志物,提出了一种新型的基于荧光卟啉传感器阵列检测系统,并对4种肺癌呼出标志物进行检测研究。通过小波分析等数学工具对测得的荧光光谱数据进行特征提取,然后采用层次聚类、主成分分析等统计学方法对特征向量进行分析。不同体积分数的各类标志物在聚类分析中能够完全正确的聚到一起。通过主成分分析得到的前3个主成分包含了标志物的88%的信息,便能对不同类别的标志物进行识别。研究表明：该荧光卟啉传感器阵列系统能够快速有效地对不同肺癌标志物进行识别,有望在临床中得到应用。     

支持向量分类用于醚菊酯类似物构效关系的研究

将支持向量机分类方法用于醚菊酯类似物构效关系的研究，所用分子描述符为物理化学参数，包括该类化合物的两个取代基的Hammett常数σA、σB，摩尔折射MA、MB，疏水值之和П。建立了醚菊酯类似物生物活性预报的支持向量机预报模型，其留一法交叉检验的预报正确率为90．3％。将支持向量机的预报结果与主成分分析(PCA)、人工神经网络(ANN)、最近邻(KNN)的预报结果进行比较，所得SVM的预报正确率高于PCA、ANN、KNN的结果。因此，SVM方法有望成为研究药物构效关系的有力工具。     

Dynamic process fault monitoring based on neural network and PCA

A newly developed method, NNPCA, integrates two data driven techniques, neural network (NN) and principal component analysis (PCA), for process monitoring. NN is used to summarize the operating process information into a nonlinear dynamic mathematical model. Chemical dynamic processes are so complex that they are presently ahead of theoretical methods from a fundamental physical standpoint. NN functions as the nonlinear dynamic operator to remove processes' nonlinear and dynamic characteristics. PCA is employed to generate simple monitoring charts based on the multivariable residuals derived from the difference between the process measurements and the neural network prediction. It can evaluate the current performance of the process. Examples from the recent monitoring practice in the industry and the large-scale system in the Tennessee Eastman process problem are presented to help the reader delve into the matter.     

基于对角PCA的人脸识别方法

针对主成分分析(PCA)算法中存在没有提取人脸对角方向特征的问题,提出了先对人脸图像进行对角化的表示,然后利用PCA提取图像特征,最近邻法分类的人脸识别改进方法。基于ORL数据库的相关实验表明,这样的系统能够取得比传统PCA更好的识别性能。     

An adaptive neural networks formulation for the two-dimensional principal component analysis

This study, for the first time, developed an adaptive neural networks (NNs) formulation for the two-dimensional principal component analysis (2DPCA), whose space complexity is far lower than that of its statistical version. Unlike the NNs formulation of principal component analysis (PCA, i.e., 1DPCA), the solution with lower iteration in nature aims to directly deal with original image matrices. We also put forward the consistence in the conceptions of ‘eigenfaces’ or ‘eigengaits’ in both 1DPCA and 2DPCA neural networks. To evaluate the performance of the proposed NN, the experiments were carried out on AR face database and on 64 × 64 pixels gait energy images on CASIA(B) gait database. The less reconstruction error was exploited using the proposed NN in the condition of a large sample set compared to adaptive estimation of learning algorithms for NNs of PCA. On the contrary, if the sample set was small, the proposed NN could achieve a higher residue error than PCA NNs. The amount of calculation for the proposed NN here could be smaller than that for the PCA NNs on the feature extraction of the same image matrix, which represented an efficient solution to the problem of training images directly. On face and gait recognition tasks, a simple nearest neighbor classifier test indicated a particular benefit of the neural network developed here which serves as an efficient alternative to conventional PCA NNs.     

Constructing attribute weights from computer audit data for effective intrusion detection

Attributes construction and selection from audit data is the first and very important step for anomaly intrusion detection. In this paper, we present several cross frequency attribute weights to model user and program behaviors for anomaly intrusion detection. The frequency attribute weights include plain term frequency (TF) and various forms of term frequency-inverse document frequency (tfidf), referred to as Ltfidf, Mtfidf and LOGtfidf. Nearest Neighbor (NN) and k-NN methods with Euclidean and Cosine distance measures as well as principal component analysis (PCA) and Chi-square test method based on these frequency attribute weights are used for anomaly detection. Extensive experiments are performed based on command data from Schonlau et al. The testing results show that the LOGtfidf weight gives better detection performance compared with plain frequency and other types of weights. By using the LOGtfidf weight, the simple NN method and PCA method achieve the better masquerade detection results than the other 7 methods in the literature while the Chi-square test consistently returns the worst results. The PCA method is suitable for fast intrusion detection because of its capability of reducing data dimensionality while NN and k-NN methods are suitable for detection of a small data set because of its no need of training process. A HTTP log data set collected in a real environment and the sendmail system call data from University of New Mexico (UNM) are used as well and the results also demonstrate the effectiveness of the LOGtfidf weight for anomaly intrusion detection.     

基于可视化传感器阵列系统的肺癌标志物快速检测研究

肺癌的早期快速诊断对于肺癌患者的治疗至关重要。针对肺癌患者所呼出的特定标志物,建立可视化传感器阵列系统,对4种肺癌标志物进行了实验研究。采用分层聚类分析、主成分分析的统计学方法对检测结果进行分析。对不同肺癌标志物、不同体积分数的样本在聚类分析中可以正确分类,且结构相似体积分数相近的样本能优先聚到一簇。利用主成分分析获得的前2个主成分所代表的肺癌标志物72.0%的信息量即可以实现不同类标志物样本区分。研究表明:这种可视化传感器阵列系统是一种快速有效的检测识别肺癌标志物的方法。     

基于神经网络的中文文本分类中的特征选择技术

基于神经网络的中文文本分类需要解决的核心问题是特征的选择问题,特征选择涉及选择哪些特征和选择的特征维度两个问题。针对上述问题,提出了信息增益(IG)与主成分分析(PCA)相结合的特征选择方法。通过实验比较分析了不同特征选择方法与特征维度对分类性能的影响,证明了该特征选择方法在基于神经网络的中文文本分类中的优越性,并得出神经网络的特征输入维度在200左右的时候分类性能最佳。