A classification-based framework for predicting and analyzing gene regulatory response

BACKGROUND: We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass. GeneClass is motivated by the hypothesis that in model organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular microarray experiment based on the presence of binding site subsequences ("motifs") in the gene's regulatory region and the expression levels of regulators such as transcription factors in the experiment ("parents"). GeneClass formulates the learning task as a classification problem — predicting +1 and -1 labels corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. Using the Adaboost algorithm, GeneClass learns a prediction function in the form of an alternating decision tree, a margin-based generalization of a decision tree. METHODS: In the current work, we introduce a new, robust version of the GeneClass algorithm that increases stability and computational efficiency, yielding a more scalable and reliable predictive model. The improved stability of the prediction tree enables us to introduce a detailed post-processing framework for biological interpretation, including individual and group target gene analysis to reveal condition-specific regulation programs and to suggest signaling pathways. Robust GeneClass uses a novel stabilized variant of boosting that allows a set of correlated features, rather than single features, to be included at nodes of the tree; in this way, biologically important features that are correlated with the single best feature are retained rather than decorrelated and lost in the next round of boosting. Other computational developments include fast matrix computation of the loss function for all features, allowing scalability to large datasets, and the use of abstaining weak rules, which results in a more shallow and interpretable tree. We also show how to incorporate genome-wide protein-DNA binding data from ChIP chip experiments into the GeneClass algorithm, and we use an improved noise model for gene expression data. RESULTS: Using the improved scalability of Robust GeneClass, we present larger scale experiments on a yeast environmental stress dataset, training and testing on all genes and using a comprehensive set of potential regulators. We demonstrate the improved stability of the features in the learned prediction tree, and we show the utility of the post-processing framework by analyzing two groups of genes in yeast — the protein chaperones and a set of putative targets of the Nrg1 and Nrg2 transcription factors — and suggesting novel hypotheses about their transcriptional and post-transcriptional regulation. Detailed results and Robust GeneClass source code is available for download from

Analysis of collection of hemolytic uremic syndrome-associated enterohemorrhagic Escherichia coli

Multilocus sequence typing of 169 non-O157 enterohemorrhagic Escherichia coli (EHEC) isolated from patients with hemolytic uremic syndrome (HUS) demonstrated 29 different sequence types (STs); 78.1% of these strains clustered in 5 STs. From all STs and serotypes identified, we established a reference panel of EHEC associated with HUS (HUSEC collection).</p

Phylogeny and disease association of Shiga toxin-producing Escherichia coli O91

The diversity and relatedness of 100 Shiga toxin–producing Escherichia coli O91 isolates from different patients were examined by multilocus sequence typing. We identified 10 specific sequence types (ST) and 4 distinct clonal groups. ST442 was significantly associated with hemolytic uremic syndrome

Economic Beliefs and Party Preference

This paper reports the results of a questionnaire study used to explore the economic understanding, normative positions along the egalitarian-libertarian spectrum, and the party preferences of a large student sample. The aim of the study is both to find socio-economic determinants of normative and positive beliefs and to explore how beliefs about the economy influence party support. We find that positive beliefs of lay people differ systematically from those of economic experts. Positive beliefs can be explained by high school grades, field of study, reasons for the choice of subject, personality traits, and - in part - by gender. Normative beliefs are self-serving in the sense that students whose father have high-status jobs and who seek high incomes are more libertarian than others. Party preferences are explained by the professional status of the father, religion, gender, and economic beliefs. Normative beliefs are more important for party support than positive beliefs. While there is a clear positive relation between libertarianism and support for right-leaning parties, positive beliefs only matter for some parties. A parochialism bias in positive beliefs seems to reinforce libertarian views favoring the most conservative party.Dieser Artikel berichtet die Resultate einer Umfrage, die genutzt wurde, um das ökonomische Verständnis, die normative Einstellung entlang des egalitär-libertären Spektrums und die Parteipräferenzen eines großen studentischen Samples zu untersuchen. Das Ziel der Studie ist es, sowohl die sozioökonomischen Determinanten der normativen und positiven Beliefs zu ermitteln, als auch zu untersuchen, wie diese Beliefs über die Wirtschaft die Parteipräferenz beeinflussen. Wir finden, dass die positiven Beliefs von Laien sich signifikant von denen der ökonomischen Experten unterscheiden. Die positiven Beliefs können durch Abiturnoten, Studienfachwahl, die Gründe für die Wahl des Studienfachs, Persönlichkeitsmerkmale und - zum Teil - durch das Geschlecht erklärt werden. Normative Beliefs sind einer selbstwertdienlichen Verzerrung in dem Sinne unterworfen, dass Studierende, deren Vater einer Beschäftigung mit hohem Status nachgeht und die ein hohes Einkommen anstreben, libertärer als andere sind. Parteipräferenzen werden durch den Beschäftigungsstatus des Vaters, die Religionszugehörigkeit, das Geschlecht und die ökonomischen Beliefs erklärt. Normative Beliefs sind für die Parteipräferenz wichtiger als positive Beliefs. Während es eine klare positive Beziehung zwischen Libertarismus und der Unterstützung nach rechts tendierender Parteien gibt, sind positive Beliefs nur für einige Parteien wichtig. Ein Parochialismus-Bias der positiven Beliefs scheint die libertären Ansichten zu verstärken und die konservativste Partei zu begünstigen

Modeling and verifying a broad array of network properties

Motivated by widely observed examples in nature, society and software, where groups of already related nodes arrive together and attach to an existing network, we consider network growth via sequential attachment of linked node groups, or graphlets. We analyze the simplest case, attachment of the three node V-graphlet, where, with probability alpha, we attach a peripheral node of the graphlet, and with probability (1-alpha), we attach the central node. Our analytical results and simulations show that tuning alpha produces a wide range in degree distribution and degree assortativity, achieving assortativity values that capture a diverse set of many real-world systems. We introduce a fifteen-dimensional attribute vector derived from seven well-known network properties, which enables comprehensive comparison between any two networks. Principal Component Analysis (PCA) of this attribute vector space shows a significantly larger coverage potential of real-world network properties by a simple extension of the above model when compared against a classic model of network growth.Comment: To appear in Europhysics Letter

Adaptive structure tensors and their applications

The structure tensor, also known as second moment matrix or Förstner interest operator, is a very popular tool in image processing. Its purpose is the estimation of orientation and the local analysis of structure in general. It is based on the integration of data from a local neighborhood. Normally, this neighborhood is defined by a Gaussian window function and the structure tensor is computed by the weighted sum within this window. Some recently proposed methods, however, adapt the computation of the structure tensor to the image data. There are several ways how to do that. This article wants to give an overview of the different approaches, whereas the focus lies on the methods based on robust statistics and nonlinear diffusion. Furthermore, the dataadaptive structure tensors are evaluated in some applications. Here the main focus lies on optic flow estimation, but also texture analysis and corner detection are considered

Highlights from the Pierre Auger Observatory

The Pierre Auger Observatory is the world's largest cosmic ray observatory. Our current exposure reaches nearly 40,000 km$^2$ str and provides us with an unprecedented quality data set. The performance and stability of the detectors and their enhancements are described. Data analyses have led to a number of major breakthroughs. Among these we discuss the energy spectrum and the searches for large-scale anisotropies. We present analyses of our X$_{max}$ data and show how it can be interpreted in terms of mass composition. We also describe some new analyses that extract mass sensitive parameters from the 100% duty cycle SD data. A coherent interpretation of all these recent results opens new directions. The consequences regarding the cosmic ray composition and the properties of UHECR sources are briefly discussed.Comment: 9 pages, 12 figures, talk given at the 33rd International Cosmic Ray Conference, Rio de Janeiro 201

A search for point sources of EeV photons

Measurements of air showers made using the hybrid technique developed with the fluorescence and surface detectors of the Pierre Auger Observatory allow a sensitive search for point sources of EeV photons anywhere in the exposed sky. A multivariate analysis reduces the background of hadronic cosmic rays. The search is sensitive to a declination band from -85{\deg} to +20{\deg}, in an energy range from 10^17.3 eV to 10^18.5 eV. No photon point source has been detected. An upper limit on the photon flux has been derived for every direction. The mean value of the energy flux limit that results from this, assuming a photon spectral index of -2, is 0.06 eV cm^-2 s^-1, and no celestial direction exceeds 0.25 eV cm^-2 s^-1. These upper limits constrain scenarios in which EeV cosmic ray protons are emitted by non-transient sources in the Galaxy.Comment: 28 pages, 10 figures, accepted for publication in The Astrophysical Journa