bluuebunny/crossref_metadata_2025_split huggingface dataset:
Boosting for tumor classification with gene expression data
Authors: Dettling Marcel , Bühlmann Peter 
Published: June 2003

Abstract
Motivation: Microarray experiments generate large datasets with expression values for thousands of genes but not more than a few dozens of samples. Accurate supervised classification of tissue samples in such high-dimensional problems is difficult but often crucial for successful diagnosis and treatment. A promising way to meet this challenge is by using boosting in conjunction with decision trees.
Results: We demonstrate that the generic boosting algorithm needs some modification to become an accurate classifier in the context of gene expression data. In particular, we present a feature preselection method, a more robust boosting procedure and a new approach for multi-categorical problems. This allows for slight to drastic increase in performance and yields competitive results on several publicly available datasets.
Availability: Software for the modified boosting algorithms as well as for decision trees is available for free in R at http://stat.ethz.ch/~dettling/boosting.html
Contact: dettling@stat.math.ethz.ch
* To whom correspondence should be addressed.
Coupled two-way clustering analysis of breast cancer and colon cancer
gene expression data
Authors: Getz Gad , Gal Hilah , Kela Itai , Notterman Daniel A. , Domany Eytan 
Published: June 2003

Abstract
We present and review coupled two-way clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis
Availability: Free, at http://ctwc.weizmann.ac.il
Contact: eytan.domany@weizmann.ac.il
Supplementary information: http://www.weizmann.ac.il/physics/complex/compphys/bioinfo2/
* To whom correspondence should be addressed.
Modified nonparametric approaches to detecting differentially
expressed genes in replicated microarray experiments
Authors: Zhao Yanli , Pan Wei 
Published: June 2003

Abstract
Motivation: An important goal in analyzing microarray data is to determine which genes are differentially expressed across two kinds of tissue samples or samples obtained under two experimental conditions. Various parametric tests, such as the two-sample t-test, have been used, but their possibly too strong parametric assumptions or large sample justifications may not hold in practice. As alternatives, a class of three nonparametric statistical methods, including the empirical Bayes method of Efron et al. (2001), the significance analysis of microarray (SAM) method of Tusher et al. (2001) and the mixture model method (MMM) of Pan et al. (2001), have been proposed. All the three methods depend on constructing a test statistic and a so-called null statistic such that the null statistic’s distribution can be used to approximate the null distribution of the test statistic. However, relatively little effort has been directed toward assessment of the performance or the underlying assumptions of the methods in constructing such test and null statistics
Results: We point out a problem of a current method to construct the test and null statistics, which may lead to largely inflated Type I errors (i.e. false positives). We also propose two modifications that overcome the problem. In the context of MMM, the improved performance of the modified methods is demonstrated using simulated data. In addition, our numerical results also provide evidence to support the utility and effectiveness of MMM
Contact: weip@biostat.umn.edu
* To whom correspondence should be addressed.
A statistical problem for inference to regulatory
structure from associations of gene expression measurements with
microarrays
Authors: Chu Tianjiao , Glymour Clark , Scheines Richard , Spirtes Peter 
Published: June 2003

Abstract
Motivation: One approach to inferring genetic regulatory structure from microarray measurements of mRNA transcript hybridization is to estimate the associations of gene expression levels measured in repeated samples. The associations may be estimated by correlation coefficients or by conditional frequencies (for discretized measurements) or by some other statistic. Although these procedures have been successfully applied to other areas, their validity when applied to microarray measurements has yet to be tested
Results: This paper describes an elementary statistical difficulty for all such procedures, no matter whether based on Bayesian updating, conditional independence testing, or other machine learning procedures such as simulated annealing or neural net pruning. The difficulty obtains if a number of cells from a common population are aggregated in a measurement of expression levels. Although there are special cases where the conditional associations are preserved under aggregation, in general inference of genetic regulatory structure based on conditional association is unwarranted
Contact: tchu@andrew.cmu.edu
* To whom correspondence should be addressed.
Bagging to improve the accuracy of a clustering procedure
Authors: Dudoit Sandrine , Fridlyand Jane 
Published: June 2003

Abstract
Motivation: The microarray technology is increasingly being applied in biological and medical research to address a wide range of problems such as the classification of tumors. An important statistical question associated with tumor classification is the identification of new tumor classes using gene expression profiles. Essential aspects of this clustering problem include identifying accurate partitions of the tumor samples into clusters and assessing the confidence of cluster assignments for individual samples.
Results: Two new resampling methods, inspired from bagging in prediction, are proposed to improve and assess the accuracy of a given clustering procedure. In these ensemble methods, a partitioning clustering procedure is applied to bootstrap learning sets and the resulting multiple partitions are combined by voting or the creation of a new dissimilarity matrix. As in prediction, the motivation behind bagging is to reduce variability in the partitioning results via averaging. The performances of the new and existing methods were compared using simulated data and gene expression data from two recently published cancer microarray studies. The bagged clustering procedures were in general at least as accurate and often substantially more accurate than a single application of the partitioning clustering procedure. A valuable by-product of bagged clustering are the cluster votes which can be used to assess the confidence of cluster assignments for individual observations
Contact: sandrine@stat.berkeley.edu
Supplementary information: For supplementary information on datasets, analyses, and software, consult http://www.stat.berkeley.edu/~sandrine and http://www.bioconductor.org.
* To whom correspondence should be addressed. The authors wish it to be known that, in their opinion, both authors should be regarded as joint First Authors.
Simultaneous gene clustering and subset selection for sample classification via MDL
Authors: Jörnsten Rebecka , Yu Bin 
Published: June 2003

Abstract
Motivation: The microarray technology allows for the simultaneous monitoring of thousands of genes for each sample. The high-dimensional gene expression data can be used to study similarities of gene expression profiles across different samples to form a gene clustering. The clusters may be indicative of genetic pathways. Parallel to gene clustering is the important application of sample classification based on all or selected gene expressions. The gene clustering and sample classification are often undertaken separately, or in a directional manner (one as an aid for the other). However, such separation of these two tasks may occlude informative structure in the data. Here we present an algorithm for the simultaneous clustering of genes and subset selection of gene clusters for sample classification. We develop a new model selection criterion based on Rissanen’s MDL (minimum description length) principle. For the first time, an MDL code length is given for both explanatory variables (genes) and response variables (sample class labels). The final output of the proposed algorithm is a sparse and interpretable classification rule based on cluster centroids or the closest genes to the centroids
Results: Our algorithm for simultaneous gene clustering and subset selection for classification is applied to three publicly available data sets. For all three data sets, we obtain sparse and interpretable classification models based on centroids of clusters. At the same time, these models give competitive test error rates as the best reported methods. Compared with classification models based on single gene selections, our rules are stable in the sense that the number of clusters has a small variability and the centroids of the clusters are well correlated (or consistent) across different cross validation samples. We also discuss models where the centroids of clusters are replaced with the genes closest to the centroids. These models show comparable test error rates to models based on single gene selection, but are more sparse as well as more stable. Moreover, we comment on how the inclusion of a classification criterion affects the gene clustering, bringing out class informative structure in the data
Availability: The methods presented in this paper have been implemented in the R language. The source code is available from the first author.
Contact: rebecka@stat.rutgers.edu
* To whom correspondence should be addressed.
Modular modeling of cellular systems with ProMoT/Diva
Authors: Ginkel M. , Kremling A. , Nutsch T. , Rehner R. , Gilles E. D. 
Published: June 2003

Abstract
Motivation: Need for software to setup and analyze complex mathematical models for cellular systems in a modular way, that also integrates the experimental environment of the cells
Results: A computer framework is described which allows the building of modularly structured models using an abstract, modular and general modeling methodology. With this methodology, reusable modeling entities are introduced which lead to the development of a modeling library within the modeling tool ProMot. The simulation environment Diva is used for numerical analysis and parameter identification of the models. The simulation environment provides a number of tools and algorithms to simulate and analyze complex biochemical networks. The described tools are the first steps towards an integrated computer-based modeling, simulation and visualization environment
Availability: Available on request to the authors. The software itself is free for scientific purposes but requires commercial libraries
Contact: ginkel@mpi-magdeburg.mpg.de
Supplementary information: http://www.mpi-magdeburg.mpg.de/projects/promot
* To whom correspondence should be addressed.
eXPatGen: generating dynamic expression patterns
for the systematic evaluation of analytical methods
Authors: Michaud Dennis J. , Marsh Adam G. , Dhurjati Prasad S. 
Published: June 2003

Abstract
Motivation: Experimental gene expression data sets, such as those generated by microarray or gene chip experiments, typically have significant noise and complicated interconnectivities that make understanding even simple regulatory patterns difficult. Given these complications, characterizing the effectiveness of different analysis techniques to uncover network groups and structures remains a challenge. Generating simulated expression patterns with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of different analysis methods. A simulation-based approach can systematically evaluate different gene expression analysis techniques and provide a basis for improved methods in dynamic metabolic network reconstruction
Results: We have developed an on-line simulator, called eXPatGen, to generate dynamic gene expression patterns typical of microarray experiments. eXPatGen provides a quantitative network structure to represent key biological features, including the induction, repression, and cascade regulation of messenger RNA (mRNA). The simulation is modular such that the expression model can be replaced with other representations, depending on the level of biological detail required by the user. Two example gene networks, of 25 and 100 genes respectively, were simulated. Two standard analysis techniques, clustering and PCA analysis, were performed on the resulting expression patterns in order to demonstrate how the simulator might be used to evaluate different analysis methods and provide experimental guidance for biological studies of gene expression
Availability: http://www.che.udel.edu/eXPatGen/
Contact: dhurjati@che.udel.edu
* To whom correspondence should be addressed.
RASCAL: rapid scanning and correction of multiple sequence alignments
Authors: Thompson J. D. , Thierry J. C. , Poch O. 
Published: June 2003

Abstract
Motivation: Most multiple sequence alignment programs use heuristics that sometimes introduce errors into the alignment. The most commonly used methods to correct these errors use iterative techniques to maximize an objective function. We present here an alternative, knowledge-based approach that combines a number of recently developed methods into a two-step refinement process. The alignment is divided horizontally and vertically to form a‘ lattice’ in which well aligned regions can be differentiated. Alignment correction is then restricted to the less reliable regions, leading to a more reliable and efficient refinement strategy
Results: The accuracy and reliability of RASCAL is demonstrated using: (i) alignments from the BAliBASE benchmark database, where significant improvements were often observed, with no deterioration of the existing high-quality regions, (ii) a large scale study involving 946 alignments from the ProDom protein domain database, where alignment quality was increased in 68% of the cases; and (iii) an automatic pipeline to obtain a high-quality alignment of 695 full-length nuclear receptor proteins, which took 11 min on a DEC Alpha 6100 computer
Availability: RASCAL is available at ftp://ftp-igbmc.u-strasbg.fr/pub/RASCAL
Contact: poch@igbmc.u-strasbg.fr
Supplementary information: http://bioinfo-igbmc.u-strasbourg.fr/BioInfo/RASCAL/paper/rascal_supp.html
* To whom correspondence should be addressed.
The Effect of Corporate Governance on the Use of Enterprise Risk Management: Evidence From Canada
Authors: Kleffner Anne E. , Lee Ryan B. , McGannon Bill 
Published: June 2003

AbstractThis article examines the use of enterprise risk management (ERM) by companies in Canada, the characteristics that are associated with the use of ERM, what obstacles companies face in implementing ERM, and what role, if any, corporate governance guidelines have played in the decision to adopt ERM. We obtained our data from the responses to a mail survey sent to Canadian Risk and Insurance Management Society members as well as telephone interviews with 19 of the respondents. The results indicate that 31 percent of the sample had adopted ERM and that reasons for adopting ERM include the influence of the risk manager (61 percent), encouragement from the board of directors (51 percent), and compliance with Toronto Stock Exchange (TSE) guidelines (37 percent). The major deterrents to ERM were an organizational structure that discourages ERM and an overall resistance to change. Although only about one‐third of companies indicated that they had adopted an ERM approach, evidence was clear that a larger portion of the sample was moving in that direction, as indicated by what changes they had observed in their companies in the past three years. These include the development of company‐wide guidelines for risk management (45 percent), an increased awareness of nonoperational risks by operational risk management personnel and an increased awareness of operational risks by nonoperational risk management personnel (49 percent), more coordination with different areas responsible for risk management (64 percent), and more involvement and interaction in the decision making of other departments. Contrary to what we expected, there was not a significant difference between firms that are listed on the TSE versus those that are not in terms of the propensity to use ERM. However, the fact that 37 percent of firms indicated that the TSE guidelines were influential in their decision to adopt ERM provides some evidence that the guidelines are influencing companies’ risk management strategies.
Insurance and China's Entry Into the WTO
Authors: D'Arcy Stephen P. , Xia Hui 
Published: June 2003

AbstractInsurance emerged as one of the most hotly debated issues regarding China's entry into the World Trade Organization (WTO) as a result of both historical precedents and the potential for significant market growth. The new rules for insurance operations in China under the WTO will allow insurers more freedom to enter and operate in this market. This article provides an explanation of the history of insurance operations in China, the role of insurance in the WTO negotiation process, and the effect that membership in the WTO will have on insurance operations in China in the future.
Sociability and Positive Emotionality: Genetic and Environmental Contributions to the Covariation Between Different Facets of Extraversion
Authors: Eid Michael , Riemann Rainer , Angleitner Alois , Borkenau Peter 
Published: June 2003

AbstractThe relation between sociability and positive affect is one of the most often replicated results of research on personality and subjective well‐being. It is shown how behavior genetics can contribute to our understanding of the covariance between sociability and positive emotionality. The results of a multimethod behavior‐genetic study with 158 monozygotic and 120 dizygotic twins are reported. In this study, sociability and two components of positive emotionality (positive affect, energy) were assessed by self‐report and other report. Additionally, positive state affect was assessed in five situations and aggregated across situations. The results showed that there are strong genetic correlations between all variables. Furthermore, there are substantive correlations between the nonshared environmental components of the different variables. Shared environmental influences, however, seemed to be unimportant for explaining the correlations between sociability and the different components of positive emotionality. The results are discussed with respect to their implications for future research on sociability and positive emotionality.
Board Composition and Performance in Life Insurance Companies
Authors: O'Sullivan Noel , Diacon Stephen R. 
Published: June 2003

AbstractThe past decade has witnessed a renewed emphasis on the quality of board governance worldwide. In the wake of a series of governance reports, particular interest has focused on the role and effectiveness of non‐executive board members. This study seeks to add to our understanding of the governance role of non‐executive directors by examining the use and usefulness of non‐executives in insurance companies. The focus on the insurance industry, which includes both proprietary (stock) and mutual companies, allows us to examine the importance of board governance in the context of different ownership structures. Furthermore, by focusing our study in a period prior to the widespread adoption of the recommendations of the Cadbury Report by UK companies, our findings should more accurately capture the true role of board composition in a less‐prescriptive governance environment. Our results suggest that mutual insurers utilize a greater proportion of non‐executive directors and are less likely to have CEO/Chairman duality than their proprietary counterparts. This evidence is consistent with mutuals using stronger board governance to compensate for weaker ownership control. Proprietary companies, which are subject to stronger shareholder and capital market control, place less reliance on non‐executive monitoring. Using a number of performance measures, we find no significant difference in the behaviour of mutual and proprietary companies with the exception of executive remuneration, (which is significantly higher for proprietary companies); furthermore there is no evidence that mutuals have outperformed their stock company rivals. Overall, our findings suggest that insurance companies emphasize different governance mechanisms depending on the specific monitoring problems they face.
Receptivity to Change in a General Medical Practice
Authors: Newton John , Graham Joanne , McLoughlin Kevin , Moore Alice 
Published: June 2003

AbstractIn this paper, Pettigrew, Ferlie and McKee's concepts of receptive and non receptive contexts for change are examined using data from an evaluation study of a general medical practice implementing a Personal Medical Services Pilot between 1998 and 2001. Four questions concerning the applicability, associatedness, temporality and dynamism of the eight factors Pettigrew et al. identified as constitutive of receptivity are used in the analysis, which extends the original model through an operationalization yielding 21 ‘focal questions’. Although the process of change at the fieldwork site was stalled, the receptivity model is shown to have been a useful tool in identifying the factors blocking change. The analysis shows that when the eight factors are conceptualized in terms of the strength, direction and continuity of their influence, the receptivity metaphor provides a distinctive tool for the analysis of change. The paper concludes that further refinement of the model could provide a basis for the derivation of change management typologies.
Modes of Response to Marketing Communications Failures in the Computer Services Industry
Authors: Bennett Roger 
Published: June 2003

AbstractChief executives of 186 computer services companies participated in a survey designed to establish (i) how firms in this sector were likely to respond to serious failures in their marketing communications programmes, and (ii) the factors affecting the mode of response. Businesses can react to a failure radically or conservatively. Radical responses might involve the dismissal of marketing managers, extensive reorganization of the marketing department and the implementation of radical changes in current programmes. Alternatively, a company could intensify the continuation of existing programmes; support, develop and mentor employees; and slightly modify rather than fundamentally alter its current marketing‐communications activities. A number of variables identified from academic literature concerning general organization decline and innovation were applied to the examination of corporate responses to marketing communications failures. In particular, hypotheses were developed and tested regarding the possible influences of managerial rigidity, internal power diffusion, organizational complexity and degree of formality, resource slack and decision‐making style.