Biomedical Engineering Reference
In-Depth Information
17.1.4 Lessons learned
Targetpedia has been in use for over six months, with very positive
feedback. During this time there has been much organisational change
for Pfi zer, re-affi rming the need for a central repository of target
information. Yet, in comparison with our legacy systems, Targetpedia
has changed direction in two major areas. Specifi cally, it shifts from a
'give me everything in one go' philosophy to 'give me a summary and
pointers where to go next'. Additionally, it addresses the increasing need
for social networking, particularly through shared scientifi c interest in
the molecular targets themselves.
Developing with SMW was generally a positive experience, so much so
we went on to re-use components in a second project described below.
Templating in particular is very powerful, as are the semantic capabilities
that make this system unique within its domain. Performance (in terms of
rendering the pages) was never an issue, although we did take great care
to optimise the semantic ASK queries by limiting the number of joins
across different objects in the wiki. However, the speed by which content
could be imported into the system was something that was suboptimal.
There is a considerable amount of data for over 20 000 proteins, as well
as people, diseases, projects and other entities. Loading all of this via the
MediaWiki API took around a day to perform a complete refresh. As the
API performs a number of operations in addition to loading the MySQL
database, we could not simply bypass it and insert content directly into
the database itself. Therefore, alternatives to the current loading system
may have to be found for the longer term, something that will involve
detailed analysis of the current API. Yet, even with this issue, we were
able to provide updates along a very good timeline that was acceptable to
the user community. A second area of diffi culty was correctly confi guring
the system for text searches. The MediaWiki search engine is quite
particular in how it searches the system and displays the results, which
forced us to alter the names of many pages so search results returned
titles meaningful to the user.
Vocabulary issues represented another major hurdle, and although not
the 'fault' of SMW, they did hinder the project. For instance, there are a
variety of sources that map targets to indications (OMIM, internal
project data, competitor intelligence), yet each uses a different disease
dictionary. Thus, users wish to click on 'asthma' to see all associated
proteins and targets, but this is quite diffi cult to achieve without much
laborious mapping of disease identifi ers and terms. Conversely, there are
no publically available standards around multi-protein drug targets (e.g.
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