oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2019 ( 50 )

2018 ( 268 )

2017 ( 286 )

2016 ( 453 )

Custom range...

Search Results: 1 - 10 of 223648 matches for " R. Sonnenburg "
All listed articles are free for downloading (OA Articles)
Page 1 /223648
Display every page Item
Optimized Parameter Combinations of Hydraulic Damper Modules  [PDF]
R. Sonnenburg
Journal of Transportation Technologies (JTTs) , 2014, DOI: 10.4236/jtts.2014.43025
Abstract:

This paper is devoted to the problem of finding optimized parameter combinations of automotive damper modules. Different cost functions using the amplitude spectrum of the excitation and the frequency response function of the car model will be investigated and it is shown that for three different arbitrary road excitations there exists a parameter combination of top mount stiffness, piston rod mass and damping constant that provides an optimum for the dynamic wheel load fluctuation. The achieved advantage of the optimized damper module regarding the dynamic wheel load fluctuation compared to a simple damper in a two mass vibration system can reach up to 20 percent.

Support Vector Machines and Kernels for Computational Biology
Asa Ben-Hur ,Cheng Soon Ong ,S?ren Sonnenburg,Bernhard Sch?lkopf,Gunnar R?tsch
PLOS Computational Biology , 2008, DOI: 10.1371/journal.pcbi.1000173
Abstract:
The Intestinal Microbiota and Viral Susceptibility
Justin L. Sonnenburg
Frontiers in Microbiology , 2011, DOI: 10.3389/fmicb.2011.00092
Abstract: Many infections start with microbial invasion of mucosal surfaces, which are typically colonized by a community of resident microbes. A growing body of literature demonstrates that the resident microbiota plays a significant role in host susceptibility to pathogens. Recent work has largely focused on the considerable effect that the intestinal microbiota can have upon bacterial pathogenesis. These studies reveal many significant gaps in our knowledge about the mechanisms by which the resident community impacts pathogen invasion and the nature of the ensuing host immune response. It is likely that as viral pathogens become the focus of studies that examine microbiota–host interaction, substantial effects of resident communities exerted via diverse mechanisms will be elucidated. Here we provide a perspective of the exciting emerging field that examines how the intestinal microbiota influences host susceptibility to viruses.
Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning
Gunnar R?tsch ,S?ren Sonnenburg,Jagan Srinivasan,Hanh Witte,Klaus-R Müller,Ralf-J Sommer,Bernhard Sch?lkopf
PLOS Computational Biology , 2007, DOI: 10.1371/journal.pcbi.0030020
Abstract: For modern biology, precise genome annotations are of prime importance, as they allow the accurate definition of genic regions. We employ state-of-the-art machine learning methods to assay and improve the accuracy of the genome annotation of the nematode Caenorhabditis elegans. The proposed machine learning system is trained to recognize exons and introns on the unspliced mRNA, utilizing recent advances in support vector machines and label sequence learning. In 87% (coding and untranslated regions) and 95% (coding regions only) of all genes tested in several out-of-sample evaluations, our method correctly identified all exons and introns. Notably, only 37% and 50%, respectively, of the presently unconfirmed genes in the C. elegans genome annotation agree with our predictions, thus we hypothesize that a sizable fraction of those genes are not correctly annotated. A retrospective evaluation of the Wormbase WS120 annotation [1] of C. elegans reveals that splice form predictions on unconfirmed genes in WS120 are inaccurate in about 18% of the considered cases, while our predictions deviate from the truth only in 10%–13%. We experimentally analyzed 20 controversial genes on which our system and the annotation disagree, confirming the superiority of our predictions. While our method correctly predicted 75% of those cases, the standard annotation was never completely correct. The accuracy of our system is further corroborated by a comparison with two other recently proposed systems that can be used for splice form prediction: SNAP and ExonHunter. We conclude that the genome annotation of C. elegans and other organisms can be greatly enhanced using modern machine learning technology.
Enhancing science and technology cooperation between the EU and Eastern Europe as well as Central Asia: a critical reflection on the White Paper from a S&T policy perspective
Klaus Schuch, George Bonas and J rn Sonnenburg
Journal of Innovation and Entrepreneurship , 2012, DOI: 10.1186/2192-5372-1-3
Abstract: This article reflects the main findings of the ‘White Paper on opportunities and challenges in view of enhancing the EU cooperation with Eastern Europe, Central Asia and South Caucasus in Science, Research and Innovation’, which was released in April 2012, from a science and technology (S&T) internationalisation policy perspective. In the ‘Internationalisation of R&D from an S&T policy perspective’ section of this article, the ongoing discourse on internationalisation of research and development (R&D) is discussed from an S&T policy perspective. In the ‘S&T cooperation between the EU and Eastern Europe as well as Central Asia since the early 1990s’ section, the development of S&T cooperation between the EU and EECA is described as a historical snapshot since the early 1990s. In the ‘Recent S&T internationalisation efforts of Eastern European and Central Asian countries’ section, special emphasis is given to the current EECA countries' dispositions towards R&D internationalisation. For a structured overview, the EECA region is disaggregated in three subregions, namely, (a) Russian Federation, (b) Eastern European countries (without Russia) and (c) Central Asian countries. To better position the R&D internationalisation policies of the region under scrutiny within the overall state-of-the-art of S&T, the ‘Current state of S&T in the Eastern European and Central Asian countries’ section compares main S&T indicators of the EECA countries. The ‘The White Paper recommendations in the light of international S&T cooperation policy objectives’ section finally condenses the major recommendations elaborated during the White Paper consultation process and puts them into the context of international S&T cooperation policy. The question is raised on what international cooperation can contribute to improving S&T in the EECA region and which approaches are deemed most adequate to support this. The analysis shows that most recommendations suggested in the White Paper directly target the S&T policy (delivery) system, which is put into an explicit actor's role. Science diplomacy is the identified predominant driver for deepening international R&D cooperation with the EECA region. The main instruments used are international dialogue, exchange and learning platforms, which are supported by the European Commission according to the EU's subsidiarity principle. Other S&T internationalisation policy objectives play a role too, especially if a more regionally differentiated perspective is taken into account.
Genomic and Metabolic Studies of the Impact of Probiotics on a Model Gut Symbiont and Host
Justin L. Sonnenburg,Christina T. L. Chen,Jeffrey I. Gordon
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.0040413
Abstract: Probiotics are deliberately ingested preparations of live bacterial species that confer health benefits on the host. Many of these species are associated with the fermentation of dairy products. Despite their increasing use, the molecular details of the impact of various probiotic preparations on resident members of the gut microbiota and the host are generally lacking. To address this issue, we colonized germ-free mice with Bacteroides thetaiotaomicron, a prominent component of the adult human gut microbiota, and Bifidobacterium longum, a minor member but a commonly used probiotic. Simultaneous whole genome transcriptional profiling of both bacterial species in their gut habitat and of the intestinal epithelium, combined with mass-spectrometric analysis of habitat-associated carbohydrates, revealed that the presence of B. longum elicits an expansion in the diversity of polysaccharides targeted for degradation by B. thetaiotaomicron (e.g., mannose- and xylose-containing glycans), and induces host genes involved in innate immunity. Although the overall transcriptome expressed by B. thetaiotaomicron when it encounters B. longum in the cecum is dependent upon the genetic background of the mouse (as assessed by a mixed analysis of variance [ANOVA] model of co-colonization experiments performed in NMRI and C57BL/6J animals), B. thetaiotaomicron's expanded capacity to utilize polysaccharides occurs independently of host genotype, and is also observed with a fermented dairy product-associated strain, Lactobacillus casei. This gnotobiotic mouse model provides a controlled case study of how a resident symbiont and a probiotic species adapt their substrate utilization in response to one another, and illustrates both the generality and specificity of the relationship between a host, a component of its microbiota, and intentionally consumed microbial species.
Accurate splice site prediction using support vector machines
Sonnenburg S?ren,Schweikert Gabriele,Philips Petra,Behr Jonas
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-s10-s7
Abstract: Background For splice site recognition, one has to solve two classification problems: discriminating true from decoy splice sites for both acceptor and donor sites. Gene finding systems typically rely on Markov Chains to solve these tasks. Results In this work we consider Support Vector Machines for splice site recognition. We employ the so-called weighted degree kernel which turns out well suited for this task, as we will illustrate in several experiments where we compare its prediction accuracy with that of recently proposed systems. We apply our method to the genome-wide recognition of splice sites in Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio, and Homo sapiens. Our performance estimates indicate that splice sites can be recognized very accurately in these genomes and that our method outperforms many other methods including Markov Chains, GeneSplicer and SpliceMachine. We provide genome-wide predictions of splice sites and a stand-alone prediction tool ready to be used for incorporation in a gene finder. Availability Data, splits, additional information on the model selection, the whole genome predictions, as well as the stand-alone prediction tool are available for download at http://www.fml.mpg.de/raetsch/projects/splice.
Genomic and Metabolic Studies of the Impact of Probiotics on a Model Gut Symbiont and Host
Justin L Sonnenburg,Christina T. L Chen,Jeffrey I Gordon
PLOS Biology , 2006, DOI: 10.1371/journal.pbio.0040413
Abstract: Probiotics are deliberately ingested preparations of live bacterial species that confer health benefits on the host. Many of these species are associated with the fermentation of dairy products. Despite their increasing use, the molecular details of the impact of various probiotic preparations on resident members of the gut microbiota and the host are generally lacking. To address this issue, we colonized germ-free mice with Bacteroides thetaiotaomicron, a prominent component of the adult human gut microbiota, and Bifidobacterium longum, a minor member but a commonly used probiotic. Simultaneous whole genome transcriptional profiling of both bacterial species in their gut habitat and of the intestinal epithelium, combined with mass-spectrometric analysis of habitat-associated carbohydrates, revealed that the presence of B. longum elicits an expansion in the diversity of polysaccharides targeted for degradation by B. thetaiotaomicron (e.g., mannose- and xylose-containing glycans), and induces host genes involved in innate immunity. Although the overall transcriptome expressed by B. thetaiotaomicron when it encounters B. longum in the cecum is dependent upon the genetic background of the mouse (as assessed by a mixed analysis of variance [ANOVA] model of co-colonization experiments performed in NMRI and C57BL/6J animals), B. thetaiotaomicron's expanded capacity to utilize polysaccharides occurs independently of host genotype, and is also observed with a fermented dairy product-associated strain, Lactobacillus casei. This gnotobiotic mouse model provides a controlled case study of how a resident symbiont and a probiotic species adapt their substrate utilization in response to one another, and illustrates both the generality and specificity of the relationship between a host, a component of its microbiota, and intentionally consumed microbial species.
Non-Sparse Regularization for Multiple Kernel Learning
Marius Kloft,Ulf Brefeld,Soeren Sonnenburg,Alexander Zien
Computer Science , 2010,
Abstract: Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability and scalability. Unfortunately, this 1-norm MKL is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures, we generalize MKL to arbitrary norms. We devise new insights on the connection between several existing MKL formulations and develop two efficient interleaved optimization strategies for arbitrary norms, like p-norms with p>1. Empirically, we demonstrate that the interleaved optimization strategies are much faster compared to the commonly used wrapper approaches. A theoretical analysis and an experiment on controlled artificial data experiment sheds light on the appropriateness of sparse, non-sparse and $\ell_\infty$-norm MKL in various scenarios. Empirical applications of p-norm MKL to three real-world problems from computational biology show that non-sparse MKL achieves accuracies that go beyond the state-of-the-art.
The Feature Importance Ranking Measure
Alexander Zien,Nicole Kraemer,Soeren Sonnenburg,Gunnar Raetsch
Statistics , 2009,
Abstract: Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly accessible to humans and cannot easily be used to gain insights about the application domain. Therefore, one often resorts to linear models in combination with variable selection, thereby sacrificing some predictive power for presumptive interpretability. Here, we introduce the Feature Importance Ranking Measure (FIRM), which by retrospective analysis of arbitrary learning machines allows to achieve both excellent predictive performance and superior interpretation. In contrast to standard raw feature weighting, FIRM takes the underlying correlation structure of the features into account. Thereby, it is able to discover the most relevant features, even if their appearance in the training data is entirely prevented by noise. The desirable properties of FIRM are investigated analytically and illustrated in simulations.
Page 1 /223648
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.