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Red Hat Enterprise Linux on Microsoft Azure
Deploy Red Hat Enterprise Linux on Microsoft Azure for a secure, reliable, and scalable cloud environment, fully integrated with Microsoft services.
Red Hat Enterprise Linux (RHEL) on Microsoft Azure provides a secure, reliable, and flexible foundation for your cloud infrastructure. Red Hat Enterprise Linux on Microsoft Azure is ideal for enterprises seeking to enhance their cloud environment with seamless integration, consistent performance, and comprehensive support.
Analyze time-course data with significance tests, clustering, modeling
Use statistical methods to analyze time-course data (gene expression microarray and RNA-seq data in particular, but not limited to). Apply significance tests to filter out only significant genes or time series. Cluster time series into similar groups. Generate network models, including linear or non-linear models. Variable selection and optimization routines included.
Written in Scala and R. The application is a cross-platform desktop app with a simple GUI and is fully functional...
CNV Workshop is a web-enabled platform for analyzing genome variation such as copy number variation (CNV). Learn about CNV Workshop in our associated BMC Bioinformatics manuscript: http://www.biomedcentral.com/1471-2105/11/74
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Chipster’s ability to provide a biologist-friendly access to a powerful analysis platform is technically based on a desktop application user interface, a flexible distributed architecture, and the ability to integrate many types of analysis tools (command line, R/Bioconductor, Java, Web Services etc). The platform is generic and easily extendable to other areas, even beyond bioinformatics.
For more information, please see http://chipster.csc.fi and http://genomebiology.com/1471-2164/12/
Webapplication providing analysis of biomedical relationships.
Webapplication providing analysis of biomedical relationships. Built using the Grails webapplication framework (http://grails.org/) with MySQL (http://www.mysql.com/) as a back-end datastore and utilizing R (http://www.r-project.org/) for statistical analysis.
Developed by the Dana Farber Cancer Institute (http://compbio.dfci.harvard.edu/) and Entagen (http://www.entagen.com).
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