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Welcome to the XEML project page. This project aims to realize a standard for the control and documentation of experimental design and growth, with a maximum of power in terms of data processing, not only to obtain reliable molecular and physiological data, but also to make plant
The Ontology Working Group (OWG) is part of the Metabolomics Society Metabolomics Standards Initiative (MSI). It seeks to facilitate the annotation of metabolomics experiments and investigations by building a controlled vocabulary and ontology.
Framework for text mining, data integration and data analysis. Keywords: ontology and graph alignment, relation mining, warehouse, semantic database integration, bioinformatics, systems biology, microarray, Java.
EDAM is a bioinformatics tools and data ontology. Its defined terms and relationships provide a structured, controlled vocabulary to describe in semantic terms bioinformatics web services, data schema, tools, web servers, databases and so on.
The metabolomics standards initiative (msi) will develop a Core Information for Metabolomics Reporting (CIMR) recommendation and a more formal and semantically defined corresponding ontology (msi-ontology).
Bio4j is a bioinformatics graph based DB including most data available in Uniprot KB (SwissProt + Trembl), Gene Ontology (GO), UniRef (50,90,100), RefSeq, NCBI Taxonomy, and Expasy Enzyme DB.
Bio4j provides a completely new and powerful framework for protein related information querying and management. Since it relies on a high-performance graph engine, data is stored in a way that semantically represents its own structure.
MOE (Metric Optimization Engine) is an *efficient* way to optimize a system's parameters, when evaluating parameters is *time-consuming* or *expensive*.Here are some examples of when you could use MOE:* **Optimizing a system's click-through rate (CTR).** MOE is useful when evaluating CTR requires running an A/B test on real user traffic, and getting statistically significant results requires running this test for a substantial amount of time (hours, days, or even weeks).* **Optimizing tunable p
DRCAT is a catalogue of Web-based data resources for bioinformatics. It includes resource metadata (such as ID, name, description and URL) and the type, format and URL query syntax of retrievable data. DRCAT is annotated with EDAM ontology terms.
The goal of a research compendium is to provide a standard and easily recognisable way for organising a reproducible research project with R. A research compendium is ideal for projects that result in the publication of a paper because then readers of the paper can access the code and data that generated the results in the paper. A research compendium is a convention for how you organise your research artefacts into directories. The guiding principle in creating a research compendium is to organise your files following conventions that many people use. Following these conventions will help other people instantly familiarise themselves with the structure of your project, and also support tool building which takes advantage of the shared structure.Some of the earliest examples of this approach can be found in Robert Gentleman and Duncan Temple Lang's 2004 paper "Statistical Analyses and Reproducible Research" Bioconductor Project Working Papers and Gentleman's 2005 article "Reproducible Research: A Bioinformatics Case Study" in Statistical Applications in Genetics and Molecular Biology. Since then there has been a substantial increase in the use of R as a research tool in many fields, and numerous improvements in the ease of making R packages. This means that making a research compendium based on an R package is now a practical solution to the challenges of organising and communicating research results for many scientists.