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1702 Publications visible to you, out of a total of 1702
Excemplify: a flexible template based solution, parsing and managing data in spreadsheets for experimentalists.

Abstract (Expand)

In systems biology, quantitative experimental data is the basis of building mathematical models. In most of the cases, they are stored in Excel files and hosted locally. To have a public database for … collecting, retrieving and citing experimental raw data as well as experimental conditions is important for both experimentalists and modelers. However, the great effort needed in the data handling procedure and in the data submission procedure becomes the crucial limitation for experimentalists to contribute to a database, thereby impeding the database to deliver its benefit. Moreover, manual copy and paste operations which are commonly used in those procedures increase the chance of making mistakes. Excemplify, a web-based application, proposes a flexible and adaptable template-based solution to solve these problems. Comparing to the normal template based uploading approach, which is supported by some public databases, rather than predefining a format that is potentiall impractical, Excemplify allows users to create their own experiment-specific content templates in different experiment stages and to build corresponding knowledge bases for parsing. Utilizing the embedded knowledge of used templates, Excemplify is able to parse experimental data from the initial setup stage and generate following stages spreadsheets automatically. The proposed solution standardizes the flows of data traveling according to the standard procedures of applying the experiment, cuts down the amount of manual effort and reduces the chance of mistakes caused by manual data handling. In addition, it maintains the context of meta-data from the initial preparation manuscript and improves the data consistency. It interoperates and complements RightField and SEEK as well.

Authors: L. Shi, L. Jong, U. Wittig, P. Lucarelli, M. Stepath, S. Mueller, L. A. D'Alessandro, U. Klingmuller, W. Muller

Date Published: 4th Apr 2013

Publication Type: Journal

The Shape of Protein Crowders is a Major Determinant of Protein Diffusion

Abstract

Not specified

Authors: Jessica Balbo, Paolo Mereghetti, Dirk-Peter Herten, Rebecca C. Wade

Date Published: 1st Apr 2013

Publication Type: Journal

Translational repression of thymidylate synthase by targeting its mRNA

Abstract

Not specified

Authors: Divita Garg, Alexander V. Beribisky, Glauco Ponterini, Alessio Ligabue, Gaetano Marverti, Andrea Martello, M. Paola Costi, Michael Sattler, Rebecca C. Wade

Date Published: 1st Apr 2013

Publication Type: Journal

Biggest challenges in bioinformatics

Abstract (Expand)

The third Heidelberg Unseminars in Bioinformatics (HUB) was held on 18th October 2012, at Heidelberg University, Germany. HUB brought together around 40 bioinformaticians from academia and industry to … discuss the ‘Biggest Challenges in Bioinformatics’ in a ‘World Café’ style event.

Authors: Jonathan C Fuller, Pierre Khoueiry, Holger Dinkel, Kristoffer Forslund, Alexandros Stamatakis, Joseph Barry, Aidan Budd, Theodoros G Soldatos, Katja Linssen, Abdul Mateen Rajput

Date Published: 15th Mar 2013

Publication Type: Journal

Ligand-based discovery of N-(1,3-dioxo-1H,3H-benzo[de]isochromen-5-yl)-carboxamide and sulfonamide derivatives as thymidylate synthase A inhibitors

Abstract

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Authors: Stefania Ferrari, Marco Ingrami, Fabrizia Soragni, Rebecca C. Wade, M. Paola Costi

Date Published: 1st Feb 2013

Publication Type: Journal

On GPU-Based Nearest Neighbor Queries for Large-Scale Photometric Catalogs in Astronomy

Abstract (Expand)

Nowadays astronomical catalogs contain patterns of hundreds of millions of objects with data volumes in the terabyte range. Upcoming projects will gather such patterns for several billions of objects … with peta- and exabytes of data. From a machine learning point of view, these settings often yield unsupervised, semi-supervised, or fully supervised tasks, with large training and huge test sets. Recent studies have demonstrated the effectiveness of prototype-based learning schemes such as simple nearest neighbor models. However, although being among the most computationally efficient methods for such settings (if implemented via spatial data structures), applying these models on all remaining patterns in a given catalog can easily take hours or even days. In this work, we investigate the practical effectiveness of GPU-based approaches to accelerate such nearest neighbor queries in this context. Our experiments indicate that carefully tuned implementations of spatial search structures for such multi-core devices can significantly reduce the practical runtime. This renders the resulting frameworks an important algorithmic tool for current and upcoming data analyses in astronomy.

Authors: Justin Heinermann, Oliver Kramer, Kai Lars Polsterer, Fabian Gieseke

Date Published: 2013

Publication Type: InProceedings

Finding new high-redshift quasars by asking the neighbours

Abstract

Not specified

Authors: Kai Lars Polsterer, Peter-Christian Zinn, Fabian Gieseke

Date Published: 2013

Publication Type: Journal

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