A marginal model approach for analysis of multi-reader multi-test receiver operating characteristic (ROC) data

The receiver operating characteristic curve is a popular tool to characterize the capabilities of diagnostic tests with continuous or ordinal responses. One common design for assessing the accuracy of diagnostic tests involves multiple readers and multiple tests, in which all readers read all test results from the same patients. This design is most commonly used in a radiology setting, where the results of diagnostic tests depend on a radiologist's subjective interpretation. The most widely used approach for analyzing data from such a study is the Dorfman-Berbaum-Metz (DBM) method (Dorfman et al., 1992) which utilizes a standard analysis of variance (ANOVA) model for the jackknife pseudovalues of the area under the ROC curves (AUCs). Although the DBM method has performed well in published simulation studies, there is no clear theoretical basis for this approach. In this paper, focusing on continuous outcomes, we investigate its theoretical basis. Our result indicates that the DBM method does not satisfy the regular assumptions for standard ANOVA models, and thus might lead to erroneous inference. We then propose a marginal model approach based on the AUCs which can adjust for covariates as well. Consistent and asymptotically normal estimators are derived for regression coefficients. We compare our approach with the DBM method via simulation and by an application to data from a breast cancer study. The simulation results show that both our method and the DBM method perform well when the accuracy of tests under the study is the same and that our method outperforms the DBM method for inference on individual AUCs when the accuracy of tests is not the same. The marginal model approach can be easily extended to ordinal outcomes.     

一类神经传播方程的特征差分方法和最佳阶l^2误差估计

给出矩形域上一类神经传播方程的特征差分,利用沿特征线方向构造差分逼近格式的方法和技巧．对给定的模型进行离散数值逼近和数值分析．特别是在沿特征线方向构造离散差分格式的过程中,可能会出现离散点在定义域之外的问题．本文提供了一个新的有效的差分逼近的处理方法,得到了该方法的三。一模误差估计．     

Regional Principal Color Based Saliency Detection

Saliency detection is widely used in many visual applications like image segmentation, object recognition and classification. In this paper, we will introduce a new method to detect salient objects in natural images. The approach is based on a regional principal color contrast modal, which incorporates low-level and medium-level visual cues. The method allows a simple computation of color features and two categories of spatial relationships to a saliency map, achieving higher F-measure rates. At the same time, we present an interpolation approach to evaluate resulting curves, and analyze parameters selection. Our method enables the effective computation of arbitrary resolution images. Experimental results on a saliency database show that our approach produces high quality saliency maps and performs favorably against ten saliency detection algorithms.     

Comparison of longitudinal variance components and regression-based approaches for linkage detection on chromosome 17 for systolic blood pressure

We compare two methods to detect genetic linkage by using serial observations of systolic blood pressure in pedigree data from the Framingham Heart Study focusing on chromosome 17. The first method is a variance components (VC) approach that incorporates longitudinal pedigree data, and the second method is a regression-based approach that summarizes all longitudinal measures in one single measure. No evidence of linkage was found either using the VC longitudinal approach or the regression-based approach, except when all time points were used from Cohorts 1 and 2 and only subjects aged 25 and 75 years were included.     

Genome shuffling: Progress and applications for phenotype improvement

Although rational method and global technique have been successfully applied in strain improvement respectively, the demand for engineering complex phenotypes required combinatorial approach. The technology of genome shuffling has been presented as a novel whole genome engineering approach for the rapid improvement of cellular phenotypes. This approach using recursive protoplast fusion with multi-parental strains offers the advantage of recombination throughout the entire genome without the necessity for genome sequence data or network information. Genome shuffling has been demonstrated as an effective method, which is not only for producing improved strain but also for providing information on complex phenotype. In this review we attempt to present the advantage of genome shuffling, introduce the procedure of this technology, summarize the applications of this approach for phenotype improvement and then give perspective on the development of this method in the future.     

中国木质林产品碳储量及其减排潜力

伐后木质林产品作为森林生态系统碳循环的一个组成部分,对森林生态系统和大气之间的碳平衡起着至关重要的作用.为准确合理估算木质林产品碳储量和我国参加气候变化谈判提供依据,在达喀尔会议上确立的3种估算方法框架下,即:储量变化法、生产法和大气流动法,利用寿命分析法和逐步递归法以及国内参数计算我国木质林产品的碳储量,且对生产法的应用提出了一种新假设思路,并分析我国木质林产品在替代建筑材料方面的减排潜力,结果显示:①分别利用储量变化法、生产法和大气流动法估算我国1961～2004年木质林产品的碳储量变化,证明我国的木质林产品是一个碳库,并且这个碳库的碳储量不断在增长;② 3种方法估算1961～2004年木质林产品碳储量的年平均增长量分别是11.73Mt · a-1(Mt=106t)、9.31Mt · a-1和7 90Mt · a-1;③木质林产品在建筑材料领域的替代作用以及延长其使用寿命使其减排潜力增大;④从碳储量计量和估算的难易程度来看,储量变化法的应用可能对我国较为有利;⑤针对生产法假设的实践应用,还需要进一步的研究.     

A new family-based association test via a least-squares method

To test the association between a dichotomous phenotype and genetic marker based on family data, we propose a least-squares method using the vector of phenotypes and their cross products within each family. This new approach allows covariate adjustment and is numerically much simpler to implement compared to likelihood- based methods. The new approach is asymptotically equivalent to the generalized estimating equation approach with a diagonal working covariance matrix, thus avoiding some difficulties with the working covariance matrix reported previously in the literature. When applied to the data from Collaborative Study on the Genetics of Alcoholism, this new method shows a significant association between the marker rs1037475 and alcoholism.     

ESTIMATION OF SINGLE GENERATION MIGRATION DISTANCES FROM GEOGRAPHIC VARIATION IN ANIMAL MITOCHONDRIAL DNA

A new approach is introduced for the analysis of dispersal from the geographic distributions of mtDNA lineages. The method is based on the expected spatial distributions of lineages arising under a multigeneration random walk process. Unlike previous methods based on the predicted equilibria between genetic drift and gene flow, this approach is appropriate for non-equilibrium conditions, and yields an estimate of dispersal distance rather than dispersal rate. The theoretical basis for this method is examined, and an analysis of mtDNA restriction site data for Peromyscus maniculatus is presented as an example of how this approach can be applied to empirical data.     

Flexible Modelling of the Covariance Matrix in a Linear Random Effects Model

A flexible approach is proposed for modelling the covariance matrix of a linear mixed model for longitudinal data. The method combines parametric modelling of the random effects part with flexible modelling of the serial correlation component. The approach is exemplified on weight gain data and on the evolution of height of children in their first year of life of the Jimma Infant Survival Study, an Ethiopian cohort study. The analyses show the usefulness of the approach.     

Phylogenetic estimation with partial likelihood tensors

We present an alternative method for calculating likelihoods in molecular phylogenetics. Our method is based on partial likelihood tensors, which are generalizations of partial likelihood vectors, as used in Felsenstein's approach. Exploiting a lexicographic sorting and partial likelihood tensors, it is possible to obtain significant computational savings. We show this on a range of simulated data by enumerating all numerical calculations that are required by our method and the standard approach.     

An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature

The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1) Protein–protein interactions extraction, and (2) Gene–suicide association extraction. The evaluation of task (1) on the benchmark dataset (AImed corpus) showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene–suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.     

Patient-specific initial wall stress in abdominal aortic aneurysms with a backward incremental method

Patient-specific wall stress simulations on abdominal aortic aneurysms may provide a better criterion for surgical intervention than the currently used maximum transverse diameter. In these simulations, it is common practice to compute the peak wall stress by applying the full systolic pressure directly on the aneurysm geometry as it appears in medical images. Since this approach does not account for the fact that the measured geometry is already experiencing a substantial load, it may lead to an incorrect systolic aneurysm shape. We have developed an approach to compute the wall stress on the true diastolic geometry at a given pressure with a backward incremental method. The method has been evaluated with a neo-Hookean material law for several simple test problems. The results show that the method can predict an unloaded configuration if the loaded geometry and the load applied are known. The effect of incorporating the initial diastolic stress has been assessed by using three patient-specific geometries acquired with cardiac triggered MR. The comparison shows that the commonly used approach leads to an unrealistically smooth systolic geometry and therefore provides an underestimation for the peak wall stress. Our backward incremental modelling approach overcomes these issues and provides a more plausible estimate for the systolic aneurysm volume and a significantly different estimate for the peak wall stress. When the approach is applied with a more complex material law which has been proposed specifically for abdominal aortic aneurysm similar effects are observed and the same conclusion can be drawn.     

Restoration theory in models of optimal feeding behavior]

The method of stochastic dynamic programming is widely used in ecology of behavior, but has some imperfections because of use of temporal limits. The authors presented an alternative approach based on the methods of the theory of restoration. Suggested method uses cumulative energy reserves per time unit as a criterium, that leads to stationary cycles in the area of states. This approach allows to study the optimal feeding by analytic methods.     

Regularized estimation in the accelerated failure time model with high-dimensional covariates

We consider two regularization approaches, the LASSO and the threshold-gradient-directed regularization, for estimation and variable selection in the accelerated failure time model with multiple covariates based on Stute's weighted least squares method. The Stute estimator uses Kaplan-Meier weights to account for censoring in the least squares criterion. The weighted least squares objective function makes the adaptation of this approach to multiple covariate settings computationally feasible. We use V-fold cross-validation and a modified Akaike's Information Criterion for tuning parameter selection, and a bootstrap approach for variance estimation. The proposed method is evaluated using simulations and demonstrated on a real data example.     

Quantitation of immobilized proteins

A method for the quantitative determination of immobilized proteins based on the binding and subsequent elution of Coomassie Blue R is presented. Also presented is a method for the immobilization of proteins in solution by entrapment in polyacrylamide. These entrapped proteins are then available for use in the assay method presented. Other analytical procedures can also be performed on the entrapped proteins, either alone or in combination with the protein quantitation. The dye binding and elution method presented provides a sensitive and, in most applications, rapid method for the quantitative detection of immobilized proteins. Rather than immobilization being an obstacle to the assay method, this approach utilizes the advantages of immobilization for the removal of excess reagents. Application of this approach to several types of immobilized protein are presented.     

Retrospective monitoring of radioactive pollution in zones of the nuclear industry impact

An approach to the acquisition of retrospective information on the radioactive pollution of the territories to organize the radioecological monitoring of environment was proposed. The approach is based on the application of the method of dendrochronoindication, allowing one to obtain the retrospective information on the delivery and accumulation of radioactive and other chemicals in the growth rings of the trees. Statistical processing and the analysis of the arrays of the multidimensional retrospective data was carried out using the principal components method. It was shown that the approach proposed allows one to reveal the emergency emissions that took place in the past at the enterprises of nuclear industry and make a well-grounded selection of the monitoring sites to establish the radioecological monitoring in the zones of nuclear industry impact.     

Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method.

MOTIVATION: We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm. METHODS: Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples. RESULTS: The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification. AVAILABILITY: The method is available at http://dir.niehs.nih.gov/microarray/datamining CONTACT: LI3@niehs.nih.gov     

A Monte Carlo method for combined segregation and linkage analysis.

We introduce a Monte Carlo approach to combined segregation and linkage analysis of a quantitative trait observed in an extended pedigree. In conjunction with the Monte Carlo method of likelihood-ratio evaluation proposed by Thompson and Guo, the method provides for estimation and hypothesis testing. The greatest attraction of this approach is its ability to handle complex genetic models and large pedigrees. Two examples illustrate the practicality of the method. One is of simulated data on a large pedigree; the other is a reanalysis of published data previously analyzed by other methods.