Biomedical Engineering Reference
In-Depth Information
'protein kinase C', 'GABA receptor', 'gastric proton pump'). This means
that public data regarding these entities are poorly organised and diffi cult
to identify and integrate. As a consequence, we are able to present only a
small amount of information for these entities, mostly driven from
internal systems (we do, of course, provide links to the pages for the
individual protein components). We believe target information provision
would be much enhanced if there were open standards and identifi ers
available for these entities. Indeed, addressing vocabulary and identity
challenges may be key to progressing information integration and appear
to be good candidates for pre-competitive collaboration [30].
For the future, we would like to create research unit-specifi c sections
within the pages, sourced from (e.g.) disease-specifi c databases and
confi gured to appear to relevant users only. We are also investigating the
integration of 'live' data by consuming web services directly into the wiki
using available extensions [31]. We conclude that SMW provides a
powerful platform from which to deliver this and other enhancements for
our users.
17.2 The Disease Knowledge
Workbench (DKWB)
The Pfi zer Indications Discovery Research Unit (IDU) was established to
identify and explore alternative indications for compounds that have
reached late stage clinical development. The IDU is a highly collaborative
group; project leads, academic collaborators and computational scientists
work together to develop holistic views of the cellular processes and
molecular pathways within a disease of interest. Such deep understanding
allows the unit to address key areas such as patient stratifi cation,
confi dence in target rationale and identifi cation of the best therapeutic
mechanism and outcome biomarkers. Thus, fulfi lling the IDUs' mission
requires the effective management and exploitation of relevant
information across a very diverse range of diseases.
The information assessed by the IDU comes from a range of internal
and external sources, covering both raw experimental output and higher-
level information such as the biomedical literature. Interestingly, the
major informatics gap was not the management and mining of these data
per se, as these functions were already well provided for with internal
and public databases and tools. Rather, the group needed a mechanism to
bring together and disseminate the knowledge that they had assessed and
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