Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data : From Seventh International Society for Computational Biology (ISCB) Student Council Symposium 2011 Vienna, Austria. 15 July 2011

First published by BioMed Central: Schultheiss, Sebastian J.; Jean, Géraldine; Behr, Jonas; Bohnert, Regina; Drewe, Philipp; Görnitz, Nico; Kahles, André; Mudrakarta, Pramod; Sreedharan, Vipin T.; Zeller, Georg; Rätsch, Gunnar: Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data - In: BMC Bioinformatics. - ISSN 1471-2105 (online). - 12 (2011), suppl. 11, art. A7. - doi:10.1186/1471-2105-12-S11-A7

Anomaly Detection for Vision-based Railway Inspection

none7nomixedRiccardo Gasparini; Stefano Pini; Guido Borghi; Giuseppe Scaglione; Simone Calderara; Eugenio Fedeli; Rita CucchiaraRiccardo Gasparini; Stefano Pini; Guido Borghi; Giuseppe Scaglione; Simone Calderara; Eugenio Fedeli; Rita Cucchiar

Stochastic motion of test particle implies that G varies with time

The aim of this letter is to propose a new description to the time varying gravitational constant problem, which naturally implements the Dirac's large numbers hypothesis in a new proposed holographic scenario for the origin of gravity as an entropic force. We survey the effect of the Stochastic motion of the test particle in Verlinde's scenario for gravity\cite{Verlinde}. Firstly we show that we must get the equipartition values for $t\rightarrow\infty$ which leads to the usual Newtonian gravitational constant. Secondly,the stochastic (Brownian) essence of the motion of the test particle, modifies the Newton's 2'nd law. The direct result is that the Newtonian constant has been time dependence in resemblance as \cite{Running}.Comment: Accepted in International Journal of Theoretical Physic

Interactive Anomaly Detection Based on Clustering and Online Mirror Descent

In several applications, when anomalies are detected, human experts have to investigate or verify them one by one. As they investigate, they unwittingly produce a label - true positive (TP) or false positive (FP). In this paper, we propose a method (called OMD-Clustering) that exploits this label feedback to minimize the FP rate and detect more relevant anomalies, while minimizing the expert effort required to inves- tigate them. The OMD-Clustering method iteratively suggests the top-1 anomalous instance to a human expert and receives feedback. Before suggesting the next anomaly, the method re-ranks instances so that the top anomalous instances are similar to the TP instances and dissimi- lar to the FP instances. This is achieved by learning to score anomalies differently in various regions of the feature space. An experimental eval- uation on several real-world datasets is conducted. The results show that OMD-Clustering achieves significant improvement in both detection pre- cision and expert effort compared to state-of-the-art interactive anomaly detection methods

Oqtans: the RNA-seq workbench in the cloud for complete and reproducible quantitative transcriptome analysis

International audience: We present Oqtans, an open-source workbench for quantitative transcriptome analysis, that is integrated in Galaxy. Its distinguishing features include customizable computational workflows and a modular pipeline architecture that facilitates comparative assessment of tool and data quality. Oqtans integrates an assortment of machine learning-powered tools into Galaxy, which show superior or equal performance to state-of-the-art tools. Implemented tools comprise a complete transcriptome analysis workflow: short-read alignment, transcript identification/quantification and differential expression analysis. Oqtans and Galaxy facilitate persistent storage, data exchange and documentation of intermediate results and analysis workflows. We illustrate how Oqtans aids the interpretation of data from different experiments in easy to understand use cases. Users can easily create their own workflows and extend Oqtans by integrating specific tools. Oqtans is available as (i) a cloud machine image with a demo instance at cloud.oqtans.org, (ii) a public Galaxy instance at galaxy.cbio.mskcc.org, (iii) a git repository containing all installed software (oqtans.org/git); most of which is also available from (iv) the Galaxy Toolshed and (v) a share string to use along with Galaxy CloudMan. CONTACT: [email protected], [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online