The gamma-frailty Poisson model for the nonparametric estimation of panel count data

In this article, we study nonparametric estimation of the mean function of a counting process with panel observations. We introduce the gamma frailty variable to account for the intracorrelation between the panel counts of the counting process and construct a maximum pseudo-likelihood estimate with the frailty variable. Three simulated examples are given to show that this estimation procedure, while preserving the robustness and simplicity of the computation, improves the efficiency of the nonparametric maximum pseudo-likelihood estimate studied in Wellner and Zhang (2000, Annals of Statistics 28, 779-814). A real example from a bladder tumor study is used to illustrate the method.     

Estimating the accuracy of polymerase chain reaction-based tests using endpoint dilution

Polymerase chain reaction (PCR)-based tests for various microorganisms or target DNA sequences are generally acknowledged to be highly "sensitive," yet the concept of sensitivity is ill-defined in the literature on these tests. We propose that sensitivity should be expressed as a function of the number of target DNA molecules in the sample (or specificity, when the target number is 0). However, estimating this "sensitivity curve" is problematic, since it is difficult to construct samples with a fixed number of targets. Nonetheless, using serially diluted replicate aliquots of a known concentration of the target DNA sequence, we show that it is possible to disentangle random variations in the number of target DNA molecules from the underlying test sensitivity. We develop parametric, nonparametric, and semiparametric (spline-based) models for the sensitivity curve. The methods are compared on a new test for M. genitalium.     

Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines

Simultaneous multiclass classification of tumor types is essential for future clinical implementations of microarray-based cancer diagnosis. In this study, we have combined genetic algorithms (GAs) and all paired support vector machines (SVMs) for multiclass cancer identification. The predictive features have been selected through iterative SVMs/GAs, and recursive feature elimination post-processing steps, leading to a very compact cancer-related predictive gene set. Leave-one-out cross-validations yielded accuracies of 87.93% for the eight-class and 85.19% for the fourteen-class cancer classifications, outperforming the results derived from previously published methods.     

Combining electron microscopic with x-ray crystallographic structures

Analgorithm has been developed for placing three-dimensional atomic structures into appropriately scaled cryoelectron microscopy maps. The first stage in this process is to conduct a three-dimensional angular search in which the center of gravity of an X-ray crystallographically determined structure is placed on a selected position in the cryoelectron microscopy map. The quality of the fit is measured by the sum of the density at each atomic position. The second stage is to refine the three angles and three translational parameters for the best (usually 25 to 100) fits. Useful criteria for this refinement include the sum of densities at atomic sites, the lack of atoms in negative or low density, the absence of atomic clashes between symmetry-related positions of the atomic structure, and the distances between identifiable features in the map and their positions on the fitted atomic structure. These refinements generally lead to a convergence of the originally chosen, top scoring fits to just a few (about 3 to 8) acceptable possibilities. Usually, the best remaining fit is clearly superior to any of the others.     

Some insights into protein structural class prediction

It has been quite clear that the success rate for predicting protein structural class can be improved significantly by using the algorithms that incorporate the coupling effect among different amino acid components of a protein. However, there is still a lot of confusion in understanding the relationship of these advanced algorithms, such as the least Mahalanobis distance algorithm, the component-coupled algorithm, and the Bayes decision rule. In this communication, a simple, rigorous derivation is provided to prove that the Bayes decision rule introduced recently for protein structural class prediction is completely the same as the earlier component-coupled algorithm. Meanwhile, it is also very clear from the derivative equations that the least Mahalanobis distance algorithm is an approximation of the component-coupled algorithm, also named as the covariant-discriminant algorithm introduced by Chou and Elrod in protein subcellular location prediction (Protein Engineering, 1999; 12:107-118). Clarification of the confusion will help use these powerful algorithms effectively and correctly interpret the results obtained by them, so as to conduce to the further development not only in the structural prediction area, but in some other relevant areas in protein science as well.     

Statistical inference for simultaneous clustering of gene expression data

Current methods for analysis of gene expression data are mostly based on clustering and classification of either genes or samples. We offer support for the idea that more complex patterns can be identified in the data if genes and samples are considered simultaneously. We formalize the approach and propose a statistical framework for two-way clustering. A simultaneous clustering parameter is defined as a function theta=Phi(P) of the true data generating distribution P, and an estimate is obtained by applying this function to the empirical distribution P(n). We illustrate that a wide range of clustering procedures, including generalized hierarchical methods, can be defined as parameters which are compositions of individual mappings for clustering patients and genes. This framework allows one to assess classical properties of clustering methods, such as consistency, and to formally study statistical inference regarding the clustering parameter. We present results of simulations designed to assess the asymptotic validity of different bootstrap methods for estimating the distribution of Phi(P(n)). The method is illustrated on a publicly available data set.     

基于基因表达谱的肿瘤分型和特征基因选取

在分析基因表达谱数据特性的基础上,提出了一个将之用于肿瘤分子分型和选型和选取相应亚型特征基因的策略。该策略包括三个步骤：首先采用一个无监督的基因过滤算法以降低用于分型计算的数据的噪声,其次提出了一个概率模型对样本中的分类结构进行建模,最后基于聚类的结果采用相对熵的方法获得对分类贡献大的基因作为特征基因,应用该策略对两个公开发表的数据集进行了再挖掘,结果表明不但获得了其他方法可以得到的信息,而且还提供了更精细、更具有显著生物学意义的信息,具有明显的优越性。     

中国濒危鱼类、两栖爬行类和哺乳类的地理分布格局与优先保护区域——基于《中国濒危动物红皮书》的分析

《中国濒危动物红皮书》依据我国动物所面临的濒危,对我国濒危动物的濒危等级划分,种群现状,致危因素和保护措施等进行了描述说明,首批收录了535种濒危动物。本文以《中国濒危动物红皮书》中所收录的352种濒危脊椎动物（不含鸟类（以下简记为“濒危植物”）为研究对象,整理统计了现有濒危物种的分布资料,在GIS支持下,对中国濒危动物的地理分布进行了研究。结果表明,中国濒危动物物种呈明显的集聚分布,最密集的地区是横断山区,海南岛,西双版纳和云贵高原;而在华北平原,内蒙古东部,黄土高原和东北平原等地区出现大片空白区。影响濒危动物分布的主要因素有热量和水分条件,地形条件等自然条件以及历史开发,人为破坏等人文条件。山地因地形屏障作用而保留了较多的古老物种,其较为复杂的环境因子也有益物种的生存,因而物种丰富度较高。大多数动物对于水分和热量的依赖性较强,因此水热条件对其分布的限制作用十分明显。人为破坏较为严重的地区,濒危物种稀少;污染,开荒等引起的环境问题对于现存动物的威胁很。运用Dobson排除算法得到云南勐腊县等9个县分布有168种濒危动物,占全国总数（海生种类及仅分布于台湾和香港的特有种除外）的51．5％,而其土地面积之和仅为全国陆地总面积的0．9％。而云南勐腊县等94个县市就分布有中国所有的濒危动物。这些地区是我国生物多样性保护应该优先考虑的地方。     

Analysis of failure time data with dependent interval censoring

This article develops a method for the analysis of screening data for which the chance of being screened is dependent on the event of interest (informative censoring). Because not all subjects make all screening visits, the data on the failure of interest is interval censored. We propose a model that will properly adjust for the dependence to obtain an unbiased estimate of the nonparametric failure time function, and we provide an extension for applying the method for estimation of the regression parameters from a (discrete time) proportional hazards regression model. The method is applied on a data set from an observational study of cytomegalovirus shedding in a population of HIV-infected subjects who participated in a trial conducted by the AIDS Clinical Trials Group.     

Efficient multipoint mapping: making use of dominant repulsion-phase markers

The paper is devoted to the problem of multipoint gene ordering with a particular focus on "dominance" complication that acts differently in conditions of coupling-phase and repulsion-phase markers. To solve the problem we split the dataset into two complementary subsets each containing shared codominant markers and dominant markers in the coupling-phase only. Multilocus ordering in the proposed algorithm is based on pairwise recombination frequencies and using the well-known travelling salesman problem (TSP) formalization. To obtain accurate results, we developed a multiphase algorithm that includes synchronized-marker ordering of two subsets assisted by re-sampling-based map verification, combining the resulting maps into an integrated map followed by verification of the integrated map. A new synchronized Evolution-Strategy discrete optimization algorithm was developed here for the proposed multilocus ordering approach in which common codominant markers facilitate stabilization of the marker order of the two complementary maps. High performance of the employed algorithm allows systematic treatment for the problem of verification of the obtained multilocus orders, based on computing-intensive bootstrap and jackknife technologies for detection and removing unreliable marker scores. The efficiency of the proposed algorithm was demonstrated on simulated and real data.Communicated by J.W. Snape