Prediction of Protein Structural Classes by Modified Mahalanobis Discriminant Algorithm

We first discuss quantitative rules for determining the protein structural classes based on their secondary structures. Then we propose a modification of the least Mahalanobis distance method for prediction of protein classes. It is a generalization of a quadratic discriminant function to the case of degenerate covariance matrices. The resubstitution tests and leave-one-out tests are carried out to compare several methods. When the class sample sizes or the covariance matrices of different classes are significantly different, the modified method should be used to replace the least Mahalanobis distance method. Two lemmas for the derivation of our new algorithm are proved in an appendix.     

Two-Group Classification when Both Groups are Mixtures of Normals

In this paper we consider a two-group discriminant analysis problem where each group is a mixture of two subgroups. Based upon data from a clinical study of alcohol involvement and diseases, simulation experiments were performed for three different configurations of means and covariance matrices. Expected actual non-error rates are estimated for the linear, quadratic, and kernel discriminant functions for sample sizes 30, 50, 75, 100, 150 and 200. A conclusion of the article is that the kernel discriminant function performs as well as or better than quadratic discriminant function. However, the linear discriminant function was clearly inferior to either the quadratic or kernel discriminant functions.     

Bias‐Corrected Diagonal Discriminant Rules for High‐Dimensional Classification

Summary Diagonal discriminant rules have been successfully used for high‐dimensional classification problems, but suffer from the serious drawback of biased discriminant scores. In this article, we propose improved diagonal discriminant rules with bias‐corrected discriminant scores for high‐dimensional classification. We show that the proposed discriminant scores dominate the standard ones under the quadratic loss function. Analytical results on why the bias‐corrected rules can potentially improve the predication accuracy are also provided. Finally, we demonstrate the improvement of the proposed rules over the original ones through extensive simulation studies and real case studies.     

Mining HIV protease cleavage data using genetic programming with a sum-product function

MOTIVATION: In order to design effective HIV inhibitors, studying and understanding the mechanism of HIV protease cleavage specification is critical. Various methods have been developed to explore the specificity of HIV protease cleavage activity. However, success in both extracting discriminant rules and maintaining high prediction accuracy is still challenging. The earlier study had employed genetic programming with a min-max scoring function to extract discriminant rules with success. However, the decision will finally be degenerated to one residue making further improvement of the prediction accuracy difficult. The challenge of revising the min-max scoring function so as to improve the prediction accuracy motivated this study. RESULTS: This paper has designed a new scoring function called a sum-product function for extracting HIV protease cleavage discriminant rules using genetic programming methods. The experiments show that the new scoring function is superior to the min-max scoring function. AVAILABILITY: The software package can be obtained by request to Dr Zheng Rong Yang.     

基于表面肌电信号的人体动作识别算法研究进展

表面肌电信号(Surface Electromyography,sEMG)是通过相应肌群表面的传感器记录下来的一维时间序列非平稳生物电信号,不但反映了神经肌肉系统活动,对于反映相应动作肢体活动信息同样重要。而模式识别是肌电应用领域的基础和关键。为了在应用基于表面肌电信号模式识别中选取合适算法,本文拟对基于表面肌电信号的人体动作识别算法进行回顾分析,主要包括模糊模式识别算法、线性判别分析算法、人工神经网络算法和支持向量机算法。模糊模式识别能自适应提取模糊规则,对初始化规则不敏感,适合处理s EMG这样具有严格不重复的生物电信号;线性判别分析对数据进行降维,计算简单,但不适合大数据;人工神经网络可以同时描述训练样本输入输出的线性关系和非线性映射关系,可以解决复杂的分类问题,学习能力强;支持向量机处理小样本、非线性的高维数据优势明显,计算速度快。比较各方法的优缺点,为今后处理此类问题模式识别算法选取提供了参考和依据。     

Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis

A novel nonlinear biological batch process monitoring and fault identification approach based on kernel Fisher discriminant analysis (kernel FDA) is proposed. This method has a powerful ability to deal with nonlinear data and does not need to predict the future observations of variables. So it is more sensitive to fault detection. In order to improve the monitoring performance, variable trajectories of the batch processes are separated into several blocks. Then data in the original space is mapped into high-dimensional feature space via nonlinear kernel function and the optimal kernel Fisher feature vector and discriminant vector are extracted to perform process monitoring and fault identification. The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set is used to perform fault diagnosis. The proposed method is applied to the process of fed-batch penicillin fermentation simulator benchmark and shows that it can effectively capture nonlinear relationships among process variables and is more efficient than MPCA approach.     

Optimal identification procedure for Diplostomum paracaudum (Iles, 1959) and D. pseudospathaceum Niewiadomska, 1984 metacercariae (Digenea) based on morphological characters

The diagnostic usefulness of the morphological characters of the metacercariae of two similar species of the genus Diplostomum, D. paracaudum and D. pseudospathaceum, is studied. The data are based on 203 specimens of D. paracaudum and of 153 D. pseudospathaceum from fish infected experimentally. The variability of 14 morphometric features and eight indices is analysed. It appears that no single feature or index can provide a classification (discrimination) rule with a sufficiently small percentage of misclassification. In order to increase the discriminant power, a technique based on the bootstrap method is used, which, combined with a stepwise discriminant analysis, leads to the selection of five metric features. A linear discriminant function, L, obtained for selected characters separates the two species better than any single feature. It also allows each specimen to be classified as D. paracaudum if the value of the function L is positive and as D. pseudospathaceum if negative. The accuracy of this procedure is in excess of 90%.     

Microanatomy of the radius and lifestyle in amniotes (Vertebrata, Tetrapoda)

Radial cross-sections of 49 species of extant and two species of extinct amniotes of known lifestyle have been studied in order to assess the relationship between lifestyle (aquatic, amphibious or terrestrial) and bone microanatomy. Most compactness profile and body size parameters exhibit a phylogenetic signal; therefore, classical statistical tests should not be used. Permutational multiple linear regressions show an ecological signal in most compactness profile parameters and in the cross-section maximal diameter. A linear discriminant analysis is performed with these parameters to distinguish the various lifestyles. The discriminant function based on taxa of known lifestyle is used to infer the lifestyle of three extinct amniotes: the early nothosaur Pachypleurosaurus (amphibious), the therapsid Lystrosaurus (amphibious) and the synapsid Ophiacodon (aquatic). These predictions are congruent with classical palaeoecological interpretations. This model may be very useful when attempting to infer the ancestral lifestyle of amniotes and other early limbed vertebrates.     

Numerical evaluation of cytologic data. IV. Discrimination and classification

When observed data have to be assigned to one or another category, classification rules are needed. Linear discriminant functions provide easily computed rules; weighing the discriminat function according to the variances in the data sets helps reduce classification errors. Classification on the basis of a probability density involves nonlinear decision boundaries. Simple numerical examples for bivariate feature vectors are worked out to demonstrate these approaches to classification.     

Generalized Walker-Duncan Procedure for Obtaining Maximum Likelihood Estimates of Parameters of Logistic Discriminant Function

A recursive method of obtaining the maximum likelihood estimates of the parameters of the quadratic logistic discriminant function is presented. This method is an extension of the Walker and Duncan procedure (1967) proposed for the linear logistic discriminant function in a dichotomous case. A generalization of the method to the problem of discrimination between several populations is also given in the paper. It works for both linear and quadratic logistic discriminant function. After an estimation of the parameters of the logistic function a classification can be performed. An example of application of the method to automatic diagnosis of some respiratory diseases is presented. Comparison with the standard procedures used for the estimation is done by a short simulation study.     

On the Performance of Some Classification Rules for Qualitative Data for Simulated Underlying Distributions

Two linear functions for discriminating with qualitative variables (Fisher's linear discriminant function and the independence rule) are compared with the general multinomial procedure, a rule based on Lancaster's definition of higher order interactions and the quadratic discriminant function. The evaluation of these functions is carried out within Monte Carlo experiments. Various types of underlying distributions generated by a special algorithm are used.     

Minimum distance probability discriminant analysis for mixed variables

Minimum distance probability (MDP) is a robust discriminant algorithm based on a distance function. In this article, we generalize the use of MDP to the case of mixed (continuous and categorical) variables by means of the individual-score (IS) distance. This distance assumes an underlying parametric model and is based on the score transformation of the data. We have adapted it to the usual case of ignoring the distribution of the whole set of observed variables, but assuming that some knowledge about the marginal distributions is available. Finally, MDP with IS distance (IS-MDP) is compared with other discriminant methods (including those designed for mixed data) in several examples and simulations. IS-MDP is shown to be the most efficient method according the leave-one-out criterion.     

The Treatment of Missing Values in Logistic Regression

The efficiencies of the estimators in the linear logistic regression model are examined using simulations under six missing value treatments. These treatments use either the maximum likelihood or the discriminant function approach in the estimation of the regression coefficients. Missing values are assumed to occur at random. The cases of multivariate normal and dichotomous independent variables are both considered. We found that in general, there is no uniformly best method. However, mean substitution and discriminant function estimation using existing pairs of values for correlations turn out to be favourable for the cases considered.     

Choice of Smoothing Parameters for Direct Kernels in Discrimination

Direct kernels, due to LAUDER (1983), as an alternative to the indirect kernel method in discriminant analysis are considered. It is shown that direct kernels may be based on any kernel function known in discrete density estimation. The choice of smoothing parameters is based on general loss functions and a family of loss functions which are specific for the discrimination problem is introduced. Examples with distance dependent and distance independent smoothing parameters are given to illustrate the applicability.     

黄河三角洲东方白鹳繁殖期觅食栖息地的利用

2008和2009年3—6月,在黄河三角洲自然保护区采用定点观察、GPS定位、样方调查和逐步判别分析等方法对东方白鹳(Ciconia boyciana)繁殖期觅食地的利用进行了研究。共测定了75个觅食利用样方和74个对照样方的14个生态因子。结果表明,东方白鹳繁殖期倾向于在明水面、芦苇沼泽和滩地中觅食,对草地和农田利用极少。偏向于食物丰富度较高的觅食点;对隐蔽级高低没有明显的偏好。对利用样方和对照样方进行比较,发现利用样方具有植被高度和植被盖度较低,觅食地水深相对较浅,距明水面、芦苇沼泽、树林等距离较近,距重度干扰源较远等特征。逐步判别分析表明,距芦苇沼泽距离、样方内水深、距重度干扰源距离、食物丰富度和明水面距离具有重要作用,由这5个变量构成的方程在对繁殖季节东方白鹳觅食地利用样方和对照样方进行区分时,正确判别率可以达到95.5%。东方白鹳繁殖期觅食地的利用主要与水源、人为干扰和食物条件有关。     

Optimal approach for classification of acute leukemia subtypes based on gene expression data

The classification of cancer subtypes, which is critical for successful treatment, has been studied extensively with the use of gene expression profiles from oligonucleotide chips or cDNA microarrays. Various pattern recognition methods have been successfully applied to gene expression data. However, these methods are not optimal, rather they are high-performance classifiers that emphasize only classification accuracy. In this paper, we propose an approach for the construction of the optimal linear classifier using gene expression data. Two linear classification methods, linear discriminant analysis (LDA) and discriminant partial least-squares (DPLS), are applied to distinguish acute leukemia subtypes. These methods are shown to give satisfactory accuracy. Moreover, we determined optimally the number of genes participating in the classification (a remarkably small number compared to previous results) on the basis of the statistical significance test. Thus, the proposed method constructs the optimal classifier that is composed of a small size predictor and provides high accuracy.     

Phenylketonuria heterozygote detection in families with affected children

Improved approaches to the problem of heterozygote detection for phenylketonuria (PKU) were developed in this study. The discrimination was based on 85 obligate heterozygotes and 45 controls who were neither pregnant nor on birth control medication. The best separation between hetrozygotes and normals was achieved with a linear discriminant function involving the logarithms of the serum concentrations of phenylalanine, tyrosine, and tryptophan. The theoretical overlap area between the distributions of heterozygotes and controls based on the above function, was 3.75%. In the 19 obligate hetrozygotes and 13 controls who were either pregnant or on birth control medication, the best separation was achieved with a linear discriminant function involving the logarithms of the serum concentrations of phenylalanine and tyrosine. The theoretical overlap area was 8.23%. The genetic accuracy of the discriminant function was confirmed by testing the results with parental-child exclusions, segregation analysis, and the frequency of heterozygosity in nonrelated collateral spouses. Finally, there was evidence suggesting that the antihypertensive agent, aldomet, alters serum tyrosine and tryptophan levels.     

A multivariate dental sexing technique

This paper presents a method of sexing skeletal remains using dental measurements. A base sample from a population is sexed with reference to the postcranial skeleton and the dental measurements (buccal-lingual and mesial-distal diameters) are analyzed by the discriminant function technique. A linear function is derived, which will classify by sex the remaining portion of the population.     

Investigating Individual Vocal Signatures and Small-Scale Patterns of Geographic Variation in Female Bornean Gibbon (<Emphasis Type="Italic">Hylobates muelleri</Emphasis>) Great Calls

Quantifying patterns of variation in primate vocalizations has important implications for understanding the evolutionary processes that lead to variation in phenotypic traits more broadly. Here, we investigated individuality and patterns of geographic variation across a small geographic scale (ca. 10 km) in female Bornean gibbon (Hylobates muelleri) great calls. We analyzed calls recorded from wild, unhabituated gibbon groups at the Stability of Altered Forest Ecosystems site in Sabah, Malaysia. We estimated 23 acoustic features in 376 great calls from 33 different females. We used linear discriminant function analysis to investigate intra- and interindividual variation in great calls. To examine small-scale patterns of geographic variation great calls we investigated measures of acoustic dissimilarity as a function of distance. We found that temporal features (such as the duration of the notes and the duration of rest between notes) contributed substantially to individuality. We were able to identify females based on their calls with 95.7% accuracy using leave-one-out cross-validation. We found no discernible patterns of geographic variation at our site; females with neighboring territories were just as likely to have similar calls as females with more distant territories. It is possible that we did not sample across a large enough geographic range, or that substantial interindividual variation effectively swamped across-site patterns of variation. Our findings add to the growing body of evidence for individual vocal signatures in primates and mammals, but further research is needed to understand the evolutionary mechanisms that contribute to individuality in gibbon calls.