The Semantic Web for the Life Sciences (SWLS), when realized, will dramatically improve our ability to conduct bioinformatics analyses using the vast and growing stores of web-accessible resources. This ability will be achieved through the widespread acceptance and application of standards for naming, representing, describing and accessing biological information. The W3C-led Semantic Web initiative has established most, if not all, of the standards and technologies needed to achieve a unified, global SWLS. Unfortunately, the bioinformatics community has, thus far, appeared reluctant to fully adopt them. Rather, we are seeing what could be described as ‘semantic creep’—timid, piecemeal and ad hoc adoption of parts of standards by groups that should be stridently taking a leadership role for the community. We suggest that, at this point, the primary hindrances to the creation of the SWLS may be social rather than technological in nature, and that, like the original Web, the establishment of the SWLS will depend primarily on the will and participation of its consumers.

NCBI tutorial on how bead arrays work

Pubget is like Pubmed, except you get the PDFs right away

Anatomy Lens uses ontologies as knowledge bases to improve the recall of a search. Through inferencing, it is able to determine that if a user is interested in the brain, the user is also interested in the hippocampus and not the spine. To improve precision, Anatomy Lens searches over annotations on articles added by experts instead of keywords. In the above example query, an article that centrally talks about the spine but also happens to mention the keyword "brain" will be returned by a standard text search but not by Anatomy Lens. In the spirit of the semantic web, we have integrated multiple data sets and ontologies: Foundational Model of Anatomy (FMA), Gene Ontology (GO), Gene Ontology Annotations (GOA), MeSH, and PubMed. The power of Anatomy Lens's search and inferencing comes from this integration.