Robust discriminant analysis and its application to identify protein coding regions of rice genes

Identification of protein coding regions is fundamentally a statistical pattern recognition problem. Discriminant analysis is a statistical technique for classifying a set of observations into predefined classes and it is useful to solve such problems. It is well known that outliers are present in virtually every data set in any application domain, and classical discriminant analysis methods (including linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA)) do not work well if the data set has outliers. In order to overcome the difficulty, the robust statistical method is used in this paper. We choose four different coding characters as discriminant variables and an approving result is presented by the method of robust discriminant analysis.     

Identifying the 3'-terminal exon in human DNA.

MOTIVATION: We present JTEF, a new program for finding 3' terminal exons in human DNA sequences. This program is based on quadratic discriminant analysis, a standard non-linear statistical pattern recognition method. The quadratic discriminant functions used for building the algorithm were trained on a set of 3' terminal exons of type 3tuexon (those containing the true STOP codon). RESULTS: We showed that the average predictive accuracy of JTEF is higher than the presently available best programs (GenScan and Genemark.hmm) based on a test set of 65 human DNA sequences with 121 genes. In particular JTEF performs well on larger genomic contigs containing multiple genes and significant amounts of intergenic DNA. It will become a valuable tool for genome annotation and gene functional studies. AVAILABILITY: JTEF is available free for academic users on request from ftp://cshl.org/pub/science/mzhanglab/JTEF and will be made available through the World Wide Web (http://argon.cshl.org/).     

Second order discriminant analysis in chemopreventive efficacy measurement

OBJECTIVE: To describe the use of second order discriminant analysis as a classification methodology along with the underlying assumptions and sampling requirements, with special emphasis on the use of this analysis in chemopreventive efficacy studies. STUDY DESIGN: The discriminant function score distributions derived in an analysis of 2 diagnostic groups may show such overlap that a statistically significant difference in mean values cannot be shown and, more important, that a useful case-based classification cannot be attained. By using the discriminant function score distributions from each case, it is frequently possible to derive a second order discriminant function based on case-specific characteristics, rather than characteristics of nuclei, thereby attaining improved case classification. RESULTS: Second order discriminant analysis has proven very useful in the documentation of case-level efficacy in chemopreventive trials. In a study of orally administered vitamin A, a first order discriminant analysis did not achieve a statistically significant difference in the score distributions for nuclei, but a second order discriminant analysis allowed a correct recognition of intervention effects in 85% of submitted cases. In a chemopreventive study of triamcinolone, a similarly inadequate discrimination based on discriminant function scores for nuclei resulted. After a second order discriminant analysis, a reduction in solar-actinic damage could be shown in 14/15, or 93%, of treated cases. CONCLUSION: Second order discriminant analysis can be highly effective when the discriminating information offered at the nuclear level is inadequate due to high dispersion and small differences in mean values of discriminant function scores for the diagnostic groups. Second order analysis utilizes case-specific characteristics of the discriminant function score distributions to document diagnostic group separation and/or efficacy of chemopreventive intervention by a reduction in case discriminant function scores.     

Identification of EEG patterns occurring in anesthesia by means of autoregressive parameters.

In EEG analysis an automatic pattern recognition is of interest. In this paper the usefulness of autoregressive parameters to classify EEG segments recorded during anesthesia is examined. Assuming that the AR parameters are multivariate normally distributed, parametric methods of discriminant analysis can be applied. The results show that AR parameters have high discriminating power and that the lowest error classification rate (smaller than 3%) is obtained by using quadratic discriminant functions. Consequently autoregressive parameters are efficient for classifying EEG segments into general stages of anesthesia.     

Identify Beta-Hairpin Motifs with Quadratic Discriminant Algorithm Based on the Chemical Shifts

Successful prediction of the beta-hairpin motif will be helpful for understanding the of the fold recognition. Some algorithms have been proposed for the prediction of beta-hairpin motifs. However, the parameters used by these methods were primarily based on the amino acid sequences. Here, we proposed a novel model for predicting beta-hairpin structure based on the chemical shift. Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in not beta-hairpin and beta-hairpin motifs. Secondly, we used these chemical shifts as features combined with three algorithms to predict beta-hairpin structure. Finally, we achieved the best prediction, namely sensitivity of 92%, the specificity of 94% with 0.85 of Mathew’s correlation coefficient using quadratic discriminant analysis algorithm, which is clearly superior to the same method for the prediction of beta-hairpin structure from 20 amino acid compositions in the three-fold cross-validation. Our finding showed that the chemical shift is an effective parameter for beta-hairpin prediction, suggesting the quadratic discriminant analysis is a powerful algorithm for the prediction of beta-hairpin.     

Individual differences in facial configuration in large-billed crows

The recognition of individuals is a basic cognitive ability of social animals. A prerequisite for individual recognition is distinct characteristics that can be used to distinguish between other conspecific individuals. Studies of birds have shown that visual information, such as colour patterning, is used in individual recognition. However, in the case of monochromatic birds, colour patterning cannot be used to identify individuals. Therefore, we expected that the configuration of facial features, such as the shape of the bills or eyes, may have enough individuality to permit individual recognition in such species. In this study, we aimed to clarify visible individual differences in the facial configuration of large-billed crows (Corvus macrorhynchos). Specifically, we analysed the profile pictures of 16 crows. We measured 26 variables in 20 pictures of each bird and then performed principal component analysis and discriminant function analysis. The results showed that the configuration of the facial profiles was individually distinct, but re-classification by discriminant functions implied that it did not clearly differ between sexes. These results suggest that crows may be able to recognise individuals on the basis of the individuality of facial configuration, even in the absence of any conspicuous colour patterning.     

Quaternion-based discriminant analysis method for color face recognition

Pattern recognition techniques have been used to automatically recognize the objects, personal identities, predict the function of protein, the category of the cancer, identify lesion, perform product inspection, and so on. In this paper we propose a novel quaternion-based discriminant method. This method represents and classifies color images in a simple and mathematically tractable way. The proposed method is suitable for a large variety of real-world applications such as color face recognition and classification of the ground target shown in multispectrum remote images. This method first uses the quaternion number to denote the pixel in the color image and exploits a quaternion vector to represent the color image. This method then uses the linear discriminant analysis algorithm to transform the quaternion vector into a lower-dimensional quaternion vector and classifies it in this space. The experimental results show that the proposed method can obtain a very high accuracy for color face recognition.     

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。     

Pattern recognition of number gestures based on a wireless surface EMG system

Using surface electromyography (sEMG) signal for efficient recognition of hand gestures has attracted increasing attention during the last decade, with most previous work being focused on recognition of upper arm and gross hand movements and some work on the classification of individual finger movements such as finger typing tasks. However, relatively few investigations can be found in the literature for automatic classification of multiple finger movements such as finger number gestures. This paper focuses on the recognition of number gestures based on a 4-channel wireless sEMG system. We investigate the effects of three popular feature types (i.e. Hudgins’ time–domain features (TD), autocorrelation and cross-correlation coefficients (ACCC) and spectral power magnitudes (SPM)) and four popular classification algorithms (i.e. k-nearest neighbor (k-NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM)) in offline recognition. Motivated by the good performance of SVM, we further propose combining the three features and employing a new classification method, multiple kernel learning SVM (MKL-SVM). Real sEMG results from six subjects show that all combinations, except k-NN or LDA using ACCC features, can achieve above 91% average recognition accuracy, and the highest accuracy is 97.93% achieved by the proposed MKL-SVM method using the three feature combination (3F). Referring to the offline recognition results, we also implement a real-time recognition system. Our results show that all six subjects can achieve a real-time recognition accuracy higher than 90%. The number gestures are therefore promising for practical applications such as human–computer interaction (HCI).     

雌雄蚕蛹近红外反射光谱的差异及其模式识别

采用6250型近红外光谱分析仪,从波长6印到1 235nm分别对日系蚕蛹217颗、中系蚕蛹174颗、杂交种蚕蛹330颗做了扫描测试,结果表明：雌雄蚕蛹的近红外光谱有极显著的差异。从日系217颗蚕蛹中随机选取雌雄各50颗作为学习样本,采用逐步判别方法从其二阶导数光谱中抽取5个特征波长作为观测向量,利用二次型判别函数对其余621颗蚕蛹的性别进行识别,其准确率达98.7%。该方法明显优于以蚕蛹重量或大小识别蚕蛹雌雄的方法。     

Diagnostic assessment of cutaneous melanoma and common nevi using tissue counter analysis

OBJECTIVE: To test the applicability of tissue counter analysis to the diagnostic discrimination of cutaneous malignant melanoma and benign common melanocytic nevi. STUDY DESIGN: Forty cases each of melanoma and nevi were consecutively sampled. After creation of a learning set based on 20 cases each, discriminant analysis of background versus tissue elements, tumor versus other tissue elements and benign versus malignant tumor elements was performed. The discriminant functions were used to assess the amount of benign and malignant tumor elements in each case. RESULTS: In the learning set, discriminant analysis facilitated recognition of 99.6% of tissue versus background, 90.7% of tumor versus other tissue components and 85.6% of malignant versus benign tumor elements. In the whole set, the percentage of malignant tumor elements was 8.7 +/- 7.7 (range, 0.1-29.6) for benign nevi and 77.4 +/- 16.2 (43.6-99.4) for malignant melanoma. Based on these measurements, the correct diagnosis could be established in all cases (chi 2 > .0001). CONCLUSION: Tissue counter analysis may be a useful method for diagnostic purposes in histopathology.     

A pilot study of gas chromatograph/mass spectrometry-based serum metabolic profiling of colorectal cancer after operation

Colorectal cancer (CRC) is mainly depended on the radical operation, the changing energy metabolism after operation reflects the extent, the magnitude, and the degree of surgical trauma. The aim of this study was to analyse the biochemical perturbation in the serum of CRC after operation and to evaluate their involvement in the progression of CRC. Gas chromatograph-mass spectrometry (GC-MS) in combination with pattern recognition techniques (Partial least squares discriminant analysis, supervised clustering analysis) was used to analyze serum metabolome in 30 CRC patients. A 34 endogenous metabolites included amino acid, fatty acid, carbohydrate and other intermediate metabolites were identified. Partial least squares discriminant analysis based on these metabolites discriminated preoperative from postoperative CRC group. Compared with preoperative CRC patients group, decreases in l-valine, 5-oxo-l-proline, 1-deoxyglucose, d-turanose, d-maltose, arachidonic acid and hexadecanoic acid levels and increases in l-tyrosine levels were observed in postoperative CRC patients group. The result demonstrated the GC-MS technique is an valuable tool for the characterization of the metabolic perturbation, and the metabolomic study will certainly benefit for monitoring the nutrition state of CRC patients, the prognosis and therapy evaluation of CRC patients after operation.     

Applying pattern recognition methods to analyze the molecular properties of a homologous series of nitrogen mustard agents

The purpose of this research was to analyze the pharmacological properties of a homologous series of nitrogen mustard (N-mustard) agents formed after inserting 1 to 9 methylene groups (-CH₂-) between 2-N(CH₂CH₂Cl)₂ groups. These compounds were shown to have significant correlations and associations in their properties after analysis by pattern recognition methods including hierarchical classification, cluster analysis, nonmetric multi-dimensional scaling (MDS), detrended correspondence analysis, K-means cluster analysis, discriminant analysis, and self-organizing tree algorithm (SOTA) analysis. Detrended correspondence analysis showed a linear-like association of the 9 homologs, and hierarchical classification showed that each homolog had great similarity to at least one other member of the series—as did cluster analysis using paired-group distance measure. Nonmetric multi-dimensional scaling was able to discriminate homologs 2 and 3 (by number of methylene groups) from homologs 4, 5, and 6 as a group, and from homologs 7, 8, and 9 as a group. Discriminant analysis, K-means cluster analysis, and hierarchical classification distinguished the high molecular weight homologs from low molecular weight homologs. As the number of methylene groups increased the aqueous solubility decreased, dermal permeation coefficient increased, Log P increased, molar volume increased, parachor increased, and index of refraction decreased. Application of pattern recognition methods discerned useful interrelationships within the homologous series that will determine specific and beneficial clinical applications for each homolog and methods of administration.     

A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech

In this study we propose a new feature extraction algorithm, dNMF (discriminant non-negative matrix factorization), to learn subtle class-related differences while maintaining an accurate generative capability. In addition to the minimum representation error for the standard NMF (non-negative matrix factorization) algorithm, the dNMF algorithm also results in higher between-class variance for discriminant power. The multiplicative NMF learning algorithm has been modified to cope with this additional constraint. The cost function was carefully designed so that the extraction of feature coefficients from a single testing pattern with pre-trained feature vectors resulted in a quadratic convex optimization problem in non-negative space for uniqueness. It also resolves issues related to the previous discriminant NMF algorithms. The developed dNMF algorithm has been applied to the emotion recognition task for speech, where it needs to emphasize the emotional differences while de-emphasizing the dominant phonetic components. The dNMF algorithm successfully extracted subtle emotional differences, demonstrated much better recognition performance and showed a smaller representation error from an emotional speech database.     

蚕茧近红外反射（NIR）光谱的模式识别──Ⅰ．对雌雄鲜茧、死笼茧的非破坏性识别

探讨了利用蚕茧近红外反射光谱识别雌雄茧、死笼茧的方法及可行性。采用６２５０型近红外光谱分析仪,从波长６８０ｎｍ到１２３５ｎｍ对２０５颗鲜茧做了非破坏性扫描测试,用逐步判别方法从一、二阶导数光谱数据中抽取特征向量,以此特征向量建立Ｂａｙｅｓ判别函数,对３７５个检验样本进行识别,其符合率达９５．７％,该方法明显优于以茧的重量和大小判别雌雄的方法。实验结果还表明,雌雄茧近红外反射光谱的差别,主要是由于蚕蛹性质不同所致,而与茧层的关系不大。     

蚕茧近红外反射（NIR）光谱的模式识别──Ⅰ．对雌雄鲜茧、死笼茧的非破坏性识别

探讨了利用蚕茧近红外反向光谱识别雌雄茧、死笼茧的方法及可行性。采用６２５０型近红外光谱分析仪,从波长６８０ｎｍ到１２３５ｎｍ对２０５颗鲜茧做了非破坏性扫描测试,用逐步判别方法从一、二阶导数光谱数据中抽取特征向量,以此特征向量建立Ｂａｙｅｓ差别函数,对３７５个检验样本进行识别,其符合率达９５．７％,该方法明显优于以茧的重和大小差别雌雄的方法。实验结果还表明,雌雄茧近红外反射光谱的差别,主要是由于蚕肾     

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.     

Sex recognition in brown skuas: do acoustic signals matter?

Bird vocalisations are often essential for sex recognition, especially in species that show little morphological sex dimorphism. Brown skuas (Catharacta antarctica lonnbergi), which exhibit uniform plumage across both sexes, emit three main calls: the long call, the alarm call and the contact call. We tested the potential for sex recognition in brown skua calls of 42 genetically sexed individuals by analysing 8–12 acoustic parameters in the temporal and frequency domains of each call type. For every call type, we failed to find sex differences in any of the acoustic parameters measured. Stepwise discriminant function analysis (DFA) revealed that sexes cannot be unambiguously classified, with increasing uncertainty of correct classification from contact calls to long calls to alarm calls. Consequently, acoustic signalling is probably not the key mechanism for sex recognition in brown skuas.