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Welcome to LIMSwiki.org, the laboratory, health, and science informatics encyclopedia.
LIMSwiki is a collaborative wiki dedicated to the scientific community, featuring organized, documented, and up-to-date content regarding all aspects of laboratory informatics, bioinformatics, and health informatics. LIMSwiki also strives to maintain relevant knowledge about laboratory equipment as well as commercial and open-source software likely to be used in a laboratory setting. This also includes the vendors of such equipment and software. Like any community wiki, the value and success of LIMSwiki is dependent upon the scientific community contributing their vast knowledge through the creation of relevant articles and editing of existing articles where knowledge is absent. If you wish to enrich the content of this wiki or have something of value to add, please request an account and get started. |
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Get started
 Need help getting started with LIMSwiki? Have no fear! Here are some excellent starting points: LIMSwiki and MediaWiki help: |
Featured article of the week
 "Predicting biomedical metadata in CEDAR: A study of Gene Expression Omnibus (GEO)" A crucial and limiting factor in data reuse is the lack of accurate, structured, and complete descriptions of data, known as metadata. Towards improving the quantity and quality of metadata, we propose a novel metadata prediction framework to learn associations from existing metadata that can be used to predict metadata values. We evaluate our framework in the context of experimental metadata from the Gene Expression Omnibus (GEO). We applied four rule mining algorithms to the most common structured metadata elements (sample type, molecular type, platform, label type and organism) from over 1.3 million GEO records. We examined the quality of well supported rules from each algorithm and visualized the dependencies among metadata elements. Finally, we evaluated the performance of the algorithms in terms of accuracy, precision, recall, and F-measure. We found that PART is the best algorithm outperforming Apriori, Predictive Apriori, and Decision Table. All algorithms perform significantly better in predicting class values than the majority vote classifier. We found that the performance of the algorithms is related to the dimensionality of the GEO elements. (Full article...)
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Learn more
 Need to know more about laboratory and other types of informatics? Here is some suggested reading:
Compliance topics: LIMSwiki publications: |
LIMSwiki books
A LIMSwiki book is a collection of LIMSwiki articles that can be easily saved to the wiki, rendered electronically in PDF, or ordered as a printed book. Need to know more about LIMSwiki books?
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Laboratory informatics software and products
Are you looking for LIMS software? An electronic laboratory notebook? Or perhaps laboratory equipment? Try these suggestions:
Additional tools: |
LIMSwiki journal articles
A LIMSwiki journal article is a selected open-access article relating to various fields of informatics, free to republish elsewhere with proper citation. You can browse the collection using the tools below. Also note that like other articles found on this wiki, journal articles can be added to LIMSwiki books. For a full listing of journals — open-access and otherwise — relating to laboratory informatics, bioinformatics, and other forms of informatics, please see LIMSWiki:Resources/Journals. |
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Open-source software
Looking for open-source software solutions to make your lab or office more efficient? Try these locations: |
Additional LIMSwiki resources
We've created several resources for users of the wiki over the years, including the LIMSwiki informatics resource portal, which contains among other tools:
Additional tools:
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Additional information
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