Ensembles of jittered association rule classifiers

The ensembling of classifiers tends to improve predictive accuracy. To obtain an ensemble with N classifiers, one typically needs to run N learning processes. In this paper we introduce and explore Model Jittering Ensembling, where one single model is perturbed in order to obtain variants that can be used as an ensemble. We use as base classifiers sets of classification association rules. The two methods of jittering ensembling we propose are Iterative Reordering Ensembling (IRE) and Post Bagging (PB). Both methods start by learning one rule set over a single run, and then produce multiple rule sets without relearning. Empirical results on 36 data sets are positive and show that both strategies tend to reduce error with respect to the single model association rule classifier. A bias–variance analysis reveals that while both IRE and PB are able to reduce the variance component of the error, IRE is particularly effective in reducing the bias component. We show that Model Jittering Ensembling can represent a very good speed-up w.r.t. multiple model learning ensembling. We also compare Model Jittering with various state of the art classifiers in terms of predictive accuracy and computational efficiency.This work was partially supported by FCT project Rank! (PTDC/EIA/81178/2006) and by AdI project Palco3.0 financed by QREN and Fundo Europeu de Desenvolvimento Regional (FEDER), and also supported by Fundacao Ciencia e Tecnologia, FEDER e Programa de Financiamento Plurianual de Unidades de I & D. Thanks are due to William Cohen for kindly providing the executable code for the SLIPPER implementation. Our gratitude goes also to our anonymous reviewers who have helped to significantly improve this paper by sharing their knowledge and their informed criticism with the authors

Computing paths and cycles in biological interaction graphs

<p>Abstract</p> <p>Background</p> <p>Interaction graphs (signed directed graphs) provide an important qualitative modeling approach for Systems Biology. They enable the analysis of causal relationships in cellular networks and can even be useful for predicting qualitative aspects of systems dynamics. Fundamental issues in the analysis of interaction graphs are the enumeration of paths and cycles (feedback loops) and the calculation of shortest positive/negative paths. These computational problems have been discussed only to a minor extent in the context of Systems Biology and in particular the shortest signed paths problem requires algorithmic developments.</p> <p>Results</p> <p>We first review algorithms for the enumeration of paths and cycles and show that these algorithms are superior to a recently proposed enumeration approach based on elementary-modes computation. The main part of this work deals with the computation of shortest positive/negative paths, an NP-complete problem for which only very few algorithms are described in the literature. We propose extensions and several new algorithm variants for computing either exact results or approximations. Benchmarks with various concrete biological networks show that exact results can sometimes be obtained in networks with several hundred nodes. A class of even larger graphs can still be treated exactly by a new algorithm combining exhaustive and simple search strategies. For graphs, where the computation of exact solutions becomes time-consuming or infeasible, we devised an approximative algorithm with polynomial complexity. Strikingly, in realistic networks (where a comparison with exact results was possible) this algorithm delivered results that are very close or equal to the exact values. This phenomenon can probably be attributed to the particular topology of cellular signaling and regulatory networks which contain a relatively low number of negative feedback loops.</p> <p>Conclusion</p> <p>The calculation of shortest positive/negative paths and cycles in interaction graphs is an important method for network analysis in Systems Biology. This contribution draws the attention of the community to this important computational problem and provides a number of new algorithms, partially specifically tailored for biological interaction graphs. All algorithms have been implemented in the <it>CellNetAnalyzer </it>framework which can be downloaded for academic use at <url>http://www.mpi-magdeburg.mpg.de/projects/cna/cna.html</url>.</p

Stage II/III rectal cancer with intermediate response to preoperative radiochemotherapy: Do we have indications for individual risk stratification?

<p>Abstract</p> <p>Background</p> <p>Response to preoperative radiochemotherapy (RCT) in patients with locally advanced rectal cancer is very heterogeneous. Pathologic complete response (pCR) is accompanied by a favorable outcome. However, most patients show incomplete response. The aim of this investigation was to find indications for risk stratification in the group of intermediate responders to RCT.</p> <p>Methods</p> <p>From a prospective database of 496 patients with rectal adenocarcinoma, 107 patients with stage II/III cancers and intermediate response to preoperative 5-FU based RCT (ypT2/3 and TRG 2/3), treated within the German Rectal Cancer Trials were studied. Surgical treatment comprised curative (R0) total mesorectal excision (TME) in all cases. In 95 patients available for statistical analyses, residual transmural infiltration of the mesorectal compartment, nodal involvement and histolologic tumor grading were investigated for their prognostic impact on disease-free (DFS) and overall survival (OS).</p> <p>Results</p> <p>Residual tumor transgression into the mesorectal compartment (ypT3) did not influence DFS and OS rates (p = 0.619, p = 0.602, respectively). Nodal involvement after preoperative RCT (ypN1/2) turned out to be a valid prognostic factor with decreased DFS and OS (p = 0.0463, p = 0.0236, respectively). Persistent tumor infiltration of the mesorectum (ypT3) and histologic tumor grading of residual tumor cell clusters were strongly correlated with lymph node metastases after neoadjuvant treatment (p < 0.001).</p> <p>Conclusions</p> <p>Advanced transmural tumor invasion after RCT does not affect prognosis when curative (R0) resection is achievable. Residual nodal status is the most important predictor of individual outcome in intermediate responders to preoperative RCT. Furthermore, ypT stage and tumor grading turn out to be additional auxiliary factors. Future clinical trials for risk-adapted adjuvant therapy should be based on a synopsis of clinicopathologic parameters.</p

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Attenuated Food Anticipatory Activity and Abnormal Circadian Locomotor Rhythms in Rgs16 Knockdown Mice

Regulators of G protein signaling (RGS) are a multi-functional protein family, which functions in part as GTPase-activating proteins (GAPs) of G protein α-subunits to terminate G protein signaling. Previous studies have demonstrated that the Rgs16 transcripts exhibit robust circadian rhythms both in the suprachiasmatic nucleus (SCN), the master circadian light-entrainable oscillator (LEO) of the hypothalamus, and in the liver. To investigate the role of RGS16 in the circadian clock in vivo, we generated two independent transgenic mouse lines using lentiviral vectors expressing short hairpin RNA (shRNA) targeting the Rgs16 mRNA. The knockdown mice demonstrated significantly shorter free-running period of locomotor activity rhythms and reduced total activity as compared to the wild-type siblings. In addition, when feeding was restricted during the daytime, food-entrainable oscillator (FEO)-driven elevated food-anticipatory activity (FAA) observed prior to the scheduled feeding time was significantly attenuated in the knockdown mice. Whereas the restricted feeding phase-advanced the rhythmic expression of the Per2 clock gene in liver and thalamus in the wild-type animals, the above phase shift was not observed in the knockdown mice. This is the first in vivo demonstration that a common regulator of G protein signaling is involved in the two separate, but interactive circadian timing systems, LEO and FEO. The present study also suggests that liver and/or thalamus regulate the food-entrained circadian behavior through G protein-mediated signal transduction pathway(s)

Differences in iNOS and Arginase Expression and Activity in the Macrophages of Rats Are Responsible for the Resistance against T. gondii Infection

Toxoplasma gondii infects humans and warm blooded animals causing devastating disease worldwide. It has long been a mystery as to why the peritoneal macrophages of rats are naturally resistant to T. gondii infection while those of mice are not. Here, we report that high expression levels and activity of inducible nitric oxide synthase (iNOS) and low levels of arginase-1 (Arg 1) activity in the peritoneal macrophages of rats are responsible for their resistance against T. gondii infection, due to high nitric oxide and low polyamines within these cells. The opposite situation was observed in the peritoneal macrophages of mice. This discovery of the opposing functions of iNOS and Arg 1 in rodent peritoneal macrophages may lead to a better understanding of the resistance mechanisms of mammals, particularly humans and livestock, against T. gondii and other intracellular pathogens