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

Introduction

Our goal in this chapter is to understand uncertain situations, at least to the greatest extent possible. You will learn about probability and uncertainty; here is a guide to these concepts. Please interpret a probability number (from zero to one) as an exact number indicating how likely an event is to happen (representing the percentage of the time that the event would happen under similar circumstances) so that probability 0.37 represents a 37% chance that the event happens. Probability numbers can come from previous experience (relative frequency as a percentage), from a mathematical statement (theoretical probability), or can be simply made up (subjective probability, yes it is permitted to use someone’s opinion here). New events of interest to you can be defined from other events, so we have the union of two events (that happens if one or the other or both events happen, such as “at least one new product is successful”), the intersection of two events (that happens if both events happen, such as “disk failure and backup failure”), and the complement (that happens if an event does not happen). Of special interest to managers is conditional probability that shows how the probability of an event can (and must) change when new information becomes available (think about the probability of success changing when the test marketing campaign was successful). Specific rules govern how these new probabilities must be calculated, and one of the best (ie, easiest) ways to apply them is to use a structure: the probability tree (a kind of decision tree) to organize and focus your thoughts in an organized way. There are other methods for visualizing probabilities, and you will also see the Venn diagram and the joint probability table. 

Probability

We consider the Newsvendor model where uncertain demand is assumed to follow a probabilistic distribution with known functional form, but unknown parameters. These parameters are estimated, unbiasedly and consistently, from data. We show that the classic maximized expected profit expression exhibits a positive systematic bias, where realized profits are overestimated. We provide an asymptotic adjustment so that the estimate of maximized expected profit is unbiased. We also study bias in the optimal order quantity, which depends on the distribution: 1) if demand is exponentially or normally distributed, the order quantity is unbiased, 2) if demand is lognormally distributed, there is a bias in the order quantity that can be corrected. Extensive numerical experiments confirm our theoretical results.

Statistical Bias in the Newsvendor Model

In this paper we identify a statistically signiﬁcant error in naively estimating the expected proﬁt in a data-driven Newsvendor Model, and we show how to correct the error. In particular, we analyze a Newsvendor Model where the continuous demand distribution is not known, and only a sample of demand data is available. In this context, an empirical demand distribution, that is induced by the sample of data, is used in place of the (unknown) true distribution. The quantity at the critical percentile 1 − c/p of the empirical distribution is known as the Sample Average Approximation (SAA) order quantity, where p is the unit revenue and c the unit cost. We prove that, if the empirical distribution is used to estimate the expected proﬁt, this estimate exhibits a positive, statistically signiﬁcant bias. We derive a closed-form expression for this bias, that only depends on p and c , and the sample of data. We argue that the dominant source of the bias is underage, and overage plays a minimal role. Furthermore, we provide an in-depth interpretation of the bias, connecting it mathematically with a covariance term as well as visualizing the bias using areas under curves. The bias expression can then be used to design an adjusted expected proﬁt estimate, that we prove is asymptotically unbiased. Numerical hypothesis testing experiments conﬁrm that the unadjusted estimation error is statistically signiﬁcant, whereas the adjusted estimation error is not signiﬁcantly di ﬀ erent from zero. The bias is not negligible in our numerical experiments: for lognormally and normally distributed demand, the unadjusted error is 2.4% and 3.0% of the true expected proﬁt, respectively. A more detailed exploration with exact ﬁnite-sample results, for exponentially distributed demand, demonstrates that the estimation error percentage can be much larger. We also provide an extension for discrete demand distributions that, in numerical experiments, exhibits encouraging bias reduction results.

Andrew F. Siegel

Papers

Introduction

Probability

Introduction

Statistical Bias in the Newsvendor Model

Data-Driven Proﬁt Estimation Error in the Newsvendor Model