NetiNeti: discovery of scientific names from text using machine learning methods.

Background: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information.

GenBank and PubMed: How connected are they?

Background: GenBank is a public repository of all publicly available molecular sequence data from a range of sources. In addition to relevant metadata (e.g.

LigerCat: using "MeSH Clouds" from journal, article, or gene citations to facilitate the identification of relevant biomedical literature.

The identification of relevant literature from within large collections is often a challenging endeavor. In the context of indexed resources, such as MEDLINE, it has been shown that keywords from a controlled vocabulary (e.g.

Exploring historical trends using taxonomic name metadata.

Background: Authority and year information have been attached to taxonomic names since Linnaean times. The systematic structure of taxonomic nomenclature facilitates the ability to develop tools that can be used to explore historical trends that may be associated with taxonomy.

Results: From the over 10.

uBioRSS: tracking taxonomic literature using RSS.

Unlabelled: Web content syndication through standard formats such as RSS and ATOM has become an increasingly popular mechanism for publishers, news sources and blogs to disseminate regularly updated content. These standardized syndication formats deliver content directly to the subscriber, allowing them to locally aggregate content from a variety of sources instead of having to find the information on multiple websites. The uBioRSS application is a 'taxonomically intelligent' service customized for the biological sciences.