生物演化集合论中的最近共同祖先与分支分类问题的解

本文是“生物演化的数学模型”一文的续篇文章,概要如下：1）讨论最近共同祖先运算的性质;2）引进基本演化集的概念和基本演化集存在性公理;3）给出假设分类单位和导出演化集的严格定义;4）引入保持演化关系概念并讨论分支分类问题的解;5）证明分支分类问题解的存在性和唯一性。上述结果有助于为分支分类问题奠定数学理论基础。     

一类具时滞和非线性发生率的生态流行病模型的稳定性和Hopf分支

研究一类具有时滞和非线性发生率的生态流行病模型.以滞量为参数,通过分析特征方程,得到了正平衡点局部稳定和Hopf分支存在的条件.同时,应用中心流形定理和规范型理论,得到了分支方向和分支周期解的稳定性计算公式.最后对所得理论结果进行了数值模拟.     

胎盘亚纲目间关系和分支分类问题

主要根据近两年的文献,从分支系统学观点综述了现生胎盘类哺乳动物的目变动,目上组群和目间种系发生关系,给出了其19目的分支图,讨论了目上水平分支分类的几个问题。     

基于ITS序列的栓菌属部分种的分子分类初步研究

栓菌属 Trametes 的一些近缘种宏观和微观形态学非常相近,传统分类学方法难于对其进行准确分类定位。测定了 34 个分类单元的 ITS(包括 5.8SrDNA)序列,并对得到的 43 个分类单元的 ITS 序列进行系统发生分析,构建了聚类分析树状图。该树状图显示,栓菌属类群与其他属类群明显分开,Trametes versicolor 聚类到一个高支持率的独立分支。形态学上定名为 T. hirsuta 和 T. pubescens 物种聚类到同一高支持率的独立分支,试验分析表明这两个种应视为同一物种。     

一类Hodgkin-Huxley模型的分支研究

考虑一类描述肌细胞膜电位变化的Hodgkin-Huxley模型的分支问题.利用常微分方程的分支理论,结合数值模拟结果,对模型的单参数分支与双参数分支进行了讨论.分析了Bogdanov-Takens分支,得到了相应的鞍结点分支曲线,Hopf分支曲线与同宿分支曲线.     

一类具有时滞的强身型捕食模型的稳定性与Hopf分支

研究一类具有时滞和阶段结构的捕食模型.分析了正平衡点的稳定性和Hopf分支的存在性.应用中心流形定理和规范型理论,得到了确定Hopf分支方向和分支周期解稳定性的计算公式.     

非线性红松林生态系统的定性与分支研究(英文)

利用系统分析的方法研究了一类非线性红松林生态系统的稳定性,讨论了其鞍结分支和Hopf分支,且对Hopf分支周期解进行了详细的分析和计算,指出了红松林生态系统中松籽、鼠类和幼苗三种群数量具有周期波动的特征.     

葛属（Pueraria DC．）的分支分析

本文采用２４个形态状对葛属进行分支分析,得到了１１个最简约分支图和一个严格一致化分支图,葛属的种间关系,根据分支分析结果对ｖａｎ ｄｅｒ Ｍａｅｓｅｎ的葛属分类系统提出了修订意见。     

分支系统学评述

本文论述了分支分类学说的主要内容及有关问题。全文包括7方面的内容：(1)分支分类学说 的哲学原理为波普尔的证伪主义科学哲学;(2)分支分析的三个基本原则是近裔共性原则、严格单系 原则及简约性原则;(3)分支分析的工作步骤包括：单系类群的确立、性状分析、分支分析运算、分支图与分类系统的建立及分支图与性状再分析;(4)本文讨论厂分支分类学派、表型分类学派与演化 分类学派;(5)由于板块构造理论及分支分析的兴起,生物地理学发生了重大变化,出现了传统的演化生物地理学、系统发育生物地理学及替代生物地理学争鸣的局面;(6)由于杂交导致性状矛盾,因 此可以由分支分析识别杂种;(7)由科学理论的三条标准来看,分支系统学属于严格意义的科学理论。     

应用聚类分析方法研究鲤亚科鱼类的系统发育

本文把聚类分析方法与分支分类学的特征分析方法相结合,以单源群内各分类单元之间具有共同离征的多少作为聚类的依据,逐步把全部单元聚在一起。因聚类依据为共同离征的多少,因此,在一定程度上,聚类所得分浓图即为该单源群的系统发育图。按上述原理,应用于鲤亚科鱼类的系统发育研究,所得分支图与按分支分类学的简约性原则推导的分支图吻合,但应用聚类分析方法后,可使系统发育关系的推导建立在一定的数学基础上。     

Probabilistic disease classification of expression-dependent proteomic data from mass spectrometry of human serum.

We have developed an algorithm called Q5 for probabilistic classification of healthy versus disease whole serum samples using mass spectrometry. The algorithm employs principal components analysis (PCA) followed by linear discriminant analysis (LDA) on whole spectrum surface-enhanced laser desorption/ionization time of flight (SELDI-TOF) mass spectrometry (MS) data and is demonstrated on four real datasets from complete, complex SELDI spectra of human blood serum. Q5 is a closed-form, exact solution to the problem of classification of complete mass spectra of a complex protein mixture. Q5 employs a probabilistic classification algorithm built upon a dimension-reduced linear discriminant analysis. Our solution is computationally efficient; it is noniterative and computes the optimal linear discriminant using closed-form equations. The optimal discriminant is computed and verified for datasets of complete, complex SELDI spectra of human blood serum. Replicate experiments of different training/testing splits of each dataset are employed to verify robustness of the algorithm. The probabilistic classification method achieves excellent performance. We achieve sensitivity, specificity, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset. The Q5 method outperforms previous full-spectrum complex sample spectral classification techniques and can provide clues as to the molecular identities of differentially expressed proteins and peptides.     

Evaluation of Gene Expression Classification Studies: Factors Associated with Classification Performance

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.     

自然语言处理技术在生物信息学中的应用

从信息处理的角度来看,生物信息学与自然语言处理中的许多问题是非常相似的,因此,可以将一些自然语言处理中的经典方法应用到生物信息学文字中。本文介绍了自然语言处理和生物信息学中共有的问题,如比对、分类、预测等,以及这些问题的解决方法。通过对两个领域形似问题的分析可知,优秀的自然语言处理技术也可用来解决生物信息学方面的问题,并且一些还未在生物信息学领域得到应用的自然语言理解技术也有其潜在的应用价值。最后给出了一个分类问题的解决方案,演示了如何在生物数据上应用算法进行实验。     

钝头蛇属Pareas（蛇亚目：钝头蛇科）系统分类研究进展

钝头蛇属（Pareas）蛇类形态高度保守,种间形态差异微弱。以分子系统学方法为中心的整合分类方法的应用,为本类群分类难题的解决作出了重要贡献,近8年有9个新种得以描述,其中,仅在近3年就增加了7个新种,并有6个同物异名被恢复。本文依据最新研究成果,对近期钝头蛇属蛇类系统分类研究成果进行了综述,并斟酌中文种名,整理了物种名录,并编制了分类检索表。截止目前,钝头蛇属共有26种,其中在我国有分布的18种,中国特有种9种。同时,对研究中存在的问题进行了探讨,并对下一步工作提出了建议：本属物种多样性估计过低、标本采集覆盖范围不足、证据使用不甚全面,大范围密集采样以及系统发育基因组学方法的应用有助于本类群系统关系的最终解决。     

Subcellular Localization of Gram-Negative Bacterial Proteins Using Sparse Learning

One of the main challenges faced by biological applications is to predict protein subcellular localization in an automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological system into account. Traditionally such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressive Sensing), we propose a method which performs locality preserving projection with a sparseness criterion such that the feature selection and dimension reduction are merged into one analysis. The proposed sparse method decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse method is quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.     

Optimal Aggregation of Binary Classifiers for Multiclass Cancer Diagnosis Using Gene Expression Profiles

Multiclass classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. There have been many studies of aggregating binary classifiers to construct a multiclass classifier based on one-versus-the-rest (1R), one-versus-one (11), or other coding strategies, as well as some comparison studies between them. However, the studies found that the best coding depends on each situation. Therefore, a new problem, which we call the ldquooptimal coding problem,rdquo has arisen: how can we determine which coding is the optimal one in each situation? To approach this optimal coding problem, we propose a novel framework for constructing a multiclass classifier, in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. Although there is no a priori answer to the optimal coding problem, our weight tuning method can be a consistent answer to the problem. We apply this method to various classification problems including a synthesized data set and some cancer diagnosis data sets from gene expression profiling. The results demonstrate that, in most situations, our method can improve classification accuracy over simple voting heuristics and is better than or comparable to state-of-the-art multiclass predictors.     

A hierarchical method for finding optimal architecture and weights using evolutionary least square based learning

In this paper, we present a novel approach of implementing a combination methodology to find appropriate neural network architecture and weights using an evolutionary least square based algorithm (GALS).1 This paper focuses on aspects such as the heuristics of updating weights using an evolutionary least square based algorithm, finding the number of hidden neurons for a two layer feed forward neural network, the stopping criterion for the algorithm and finally some comparisons of the results with other existing methods for searching optimal or near optimal solution in the multidimensional complex search space comprising the architecture and the weight variables. We explain how the weight updating algorithm using evolutionary least square based approach can be combined with the growing architecture model to find the optimum number of hidden neurons. We also discuss the issues of finding a probabilistic solution space as a starting point for the least square method and address the problems involving fitness breaking. We apply the proposed approach to XOR problem, 10 bit odd parity problem and many real-world benchmark data sets such as handwriting data set from CEDAR, breast cancer and heart disease data sets from UCI ML repository. The comparative results based on classification accuracy and the time complexity are discussed.     

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.     

A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard

ABSTRACT: BACKGROUND: Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers. RESULTS: We compared three classifiers of protein subcellular localisation, and evaluated our estimates of sensitivity and specificity against estimates obtained using a gold standard. The method overestimated sensitivity and specificity with only a small discrepancy, and correctly ranked the classifiers. Diagnostic tests for swine flu were then compared on a small data set. Lastly, classifiers for a genome-wide association study of macular degeneration with 541094 SNPs were analysed. In all cases, run times were feasible, and results precise. The optimal logical combination of classifiers was also determined for all three data sets. Code and data are available from http://bioinformatics.monash.edu.au/downloads/. CONCLUSIONS: The examples demonstrate the methods are suitable for both small and large data sets, applicable to the wide range of bioinformatics classification problems, and robust to dependence between classifiers. In all three test cases, the globally optimal logical combination of the classifiers was found to be their union, according to three out of four ranking criteria. We propose as a general rule of thumb that the union of classifiers will be close to optimal.     

Linear mixture model approach for selecting fuzzy exponent value in fuzzy c-means algorithm

The implementations of both the supervised and unsupervised fuzzy c-means classification algorithms require a priori selection of the fuzzy exponent parameter. This parameter is a weighting exponent and it determines the degree of fuzziness of the membership grades. The determination of an optimal value for this parameter in a fuzzy classification process is problematic and remains an open problem. This paper presents a new and efficient procedure for determining a local optimal value for the fuzzy exponent in the implementation of fuzzy classification technique. Numerical results using simulated image and real data sets are used to illustrate the simplicity and effectiveness of the proposed method.