Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Interleukin 17 (IL-17) has been linked to autoimmune diseases, although its regulation and function have remained unclear. Here we have evaluated in vitro and in vivo the requirements for the differentiation of naive CD4 T cells into effector T helper cells that produce IL-17. This process required the costimulatory molecules CD28 and ICOS but was independent of the cytokines and transcription factors required for T helper type 1 or type 2 differentiation. Furthermore, both IL-4 and interferon-γ negatively regulated T helper cell production of IL-17 in the effector phase. In vivo, antibody to IL-17 inhibited chemokine expression in the brain during experimental autoimmune encephalomyelitis, whereas overexpression of IL-17 in lung epithelium caused chemokine production and leukocyte infiltration. Thus, IL-17 expression characterizes a unique T helper lineage that regulates tissue inflammation.

A distinct lineage of CD4 T cells regulates tissue inflammation by producing interleukin 17

Classical least squares regression consists of minimizing the sum of the squared residuals. Many authors have produced more robust versions of this estimator by replacing the square by something else, such as the absolute value. In this article a different approach is introduced in which the sum is replaced by the median of the squared residuals. The resulting estimator can resist the effect of nearly 50% of contamination in the data. In the special case of simple regression, it corresponds to finding the narrowest strip covering half of the observations. Generalizations are possible to multivariate location, orthogonal regression, and hypothesis testing in linear models.

Least Median of Squares Regression

Hansen and Jagannathan (HJ, 1991) describe restrictions on the volatility of stochastic discount factors (SDFs) that price a given set of asset returns. This paper compares the sampling properties of different versions of HJ bounds that use conditioning information in the form of a given set of lagged instruments. HJ describe one way to use conditioning information. Their approach is to multiply the original returns by the lagged variables, and much of the asset pricing literature to date has followed this ihmultiplicativel. approach. We also study two versions of optimized HJ bounds with conditioning information. One is from Gallant, Hansen and Tauchen (1990) and the second is based on the unconditionally-efficient portfolios derived in Ferson and Siegel (2000). We document finite-sample biases in the HJ bounds, where the biased bounds reject asset-pricing models too often. We provide useful correction factors for the bias. We also evaluate the asymptotic standard errors for the HJ bounds, from Hansen, Heaton and Luttmer (1995).

Stochastic Discount Factor Bounds with Conditioning Information

Hansen and Jagannathan (1991) (hereafter HJ) derive restrictions on the volatility of stochastic discount factors that price a given set of returns. This article studies the sampling properties of HJ bounds that use conditioning information. One approach is to multiply the returns by the lagged variables. We also study optimized HJ bounds with conditioning information from Gallant, Hansen, and Tauchen (1990) and based on portfolios derived in Ferson and Siegel (2001). We document striking finite-sample biases in the HJ bounds, where the bounds reject asset-pricing models too often. We provide a useful bias correction. We also evaluate asymptotic standard errors for the bounds from Hansen, Heaton, and Luttmer (1995). Copyright 2003, Oxford University Press.

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We mapped the genetic influences for type 1 diabetes (T1D), using 2,360 single-nucleotide polymorphism (SNP) markers in the 4.4-Mb human major histocompatibility complex (MHC) locus and the adjacent 493 kb centromeric to the MHC, initially in a survey of 363 Swedish T1D cases and controls. We confirmed prior studies showing association with T1D in the MHC, most significantly near HLA-DR/DQ. In the region centromeric to the MHC, we identified a peak of association within the inositol 1,4,5-triphosphate receptor 3 gene (ITPR3; formerly IP3R3). The most significant single SNP in this region was at the center of the ITPR3 peak of association (P=1.7×10−4 for the survey study). For validation, we typed an additional 761 Swedish individuals. The P value for association computed from all 1,124 individuals was 1.30×10−6 (recessive odds ratio 2.5; 95% confidence interval [CI] 1.7–3.9). The estimated population-attributable risk of 21.6% (95% CI 10.0%–31.0%) suggests that variation within ITPR3 reflects an important contribution to T1D in Sweden. Two-locus regression analysis supports an influence of ITPR3 variation on T1D that is distinct from that of any MHC class II gene.

Genetic mapping at 3-kilobase resolution reveals inositol 1,4,5-triphosphate receptor 3 as a risk factor for type 1 diabetes in Sweden.

Few would argue with the assertion that within the practice of comparative morphology, the comparison of shapes is fundamental to any line of synthesis in paleontology. Yet the common act of such a comparison involves a great range of judgments concerning the homology, correspondence and divergence of form of various parts of any two related shapes. Considerations of similarity must include the scalar effects of size and material, the conversion of proportions as descriptive geometric dimensions to those useful numerical values that will be represented as ratios of measured distance (relative or compared against a standard), and, by no means insignificant, the judgment about the design or functional geometry implicit in the static as well as dynamic systems being measured. This review is concerned with these considerations and with quantitative comparisons of biological shape wherein some parts of specimens obviously have changed and others parts have not.

On The Measurement of Morphology and its Change

Consider the random uniform placement of a finite number of arcs on the circle, where the arc lengths are sampled from a distribution on (0, 1). We provide exact formulae for the probability that the circle is completely covered and for the distribution of the number of uncovered gaps, extending Stevens's (1939) formulae for the case of fixed equal arc lengths. A special class of arc length distributions is considered, and exact probabilities of coverage are tabulated for the uniform distribution on (0, 1). Some asymptotic results for the number of gaps are also given.

Andrew F. Siegel

Papers

Stochastic Discount Factor Bounds with Conditioning Information

Stochastic Discount Factor Bounds with Conditioning Information

Genetic mapping at 3-kilobase resolution reveals inositol 1,4,5-triphosphate receptor 3 as a risk factor for type 1 diabetes in Sweden.

On The Measurement of Morphology and its Change

Covering the Circle with Random Arcs of Random Sizes.