W750 Nucleic Acids Research, 2006, Vol. 34, Web Server issue Figure 1. Literature search conducted in BotDB using botXminer and the Group Articles (Advanced search option). Back panel: screen shot of this BotDB search page. The end-user types in a desired word or term (words separated by logical or other operators) and selects one or more of the MEDLINE search fields and one of the six presently available terms. Middle panel: tabulated results are shown for the query ‘near ((snare, interact%), 5)’, the ‘abstract’ field and the ‘chemical’ term. The resulting list of chemical terms are tallied in the form of an alphabetically sorted table that can be expanded by clicking on a number. Front panel: the value of ‘18’ associated with the total number of articles (middle panel) is hyperlinked to these citations. entire MEDLINE XML ﬁles are stored in this database, the structure of these ﬁles is maintained, in contrast to writing new code to parse the data and to subsequently store them into relational tables. The schema-based, structured type data storage strategy is made more efﬁcient in botXminer with Oracle’s text indexing capability which makes proximity searches with operators such as ‘NEAR’, a context grammar operator, and wildcard searches feasible. The Group Articles feature of botXminer allows the user to look at a single, tabulated list of relevant grouped terms and to quickly retrieve citations of interest. Finally, botXminer has the ﬁrst user interface for MEDLINE XML ﬁles that are stored in Oracle XML DB. In comparison to other publicly available PubMed search applications, the differences exhibited by botXminer provide an opportunity for its descendants to enhance searches of the continually growing biomedical literature. In the tools described by Oliver et al. (5), MEDLINE XML ﬁles that have been downloaded are subsequently parsed and stored in schema-deﬁned relational tables. Devising appropriate schema to optimally perform all queries is a challenging task. In contrast, PubFinder (4) stores abstracts in its database and creates a reference dictionary of commonly used words. The user needs to provide PMIDs of abstracts of interest which are processed for word frequencies and compared to the reference dictionary. This natural language application seems to be comparable at some level to PubMed’s ‘related articles’ search feature. Applications, such as ChiliBot (1), PubGene (8) and MedMiner (9), require, as a minimal user-supplied input, a recognized gene name to conduct its PubMed search. GO-PubMed (2) uses the Gene Ontology vocabulary in its searches. Textpresso (10), as part of its knowledge retrieval strategy, uses a text-to-XML converter to systematically mark up sentences by its specialized ontology (that is presently based on Caenorhabditis elegans) from full-length, searchable articles. The example query provided here illustrates the power of botXminer to help the user to efﬁciently and selectively search