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for the scientists interpretation of these and other evidence to reside.
This interpretation takes the form of a 'report' comprising free text,
fi gures/images, tables, text and hyperlinks to fi les of underlying evidence.
This is, of course, very wiki-like, and the system which most matched
these needs was AlzSWAN [37], a community-driven knowledge base of
Alzheimer disease hypotheses. AlzSWAN is a wiki-based platform
that allows researchers to propose, annotate, support and refute
particular pathological mechanisms. Users attach 'statements' to a
hypothesis (based on literature, experimental data or their own
ideas), marking whether these are consistent with the hypothesis. This
mechanism to present, organise and discuss the pathophysiological
elements of the disease was very aligned with the needs of our project,
capturing at the level of scientifi c discourse, rather than individual
molecular networks.
17.2.2 Building the system
Although AlzSWAN came closest to our desired system, it has a top-
down approach to knowledge management; one provides a hypothesis
and then provides the evidence for and against it. In the case of the IDU,
the opposite was required, namely the ability to manage individual
fi ndings and ideas with the ultimate aim of bringing these together within
a fi nal therapeutic strategy. Thus, we concluded that a knowledge
structure different from AlzSWAN was needed, but still took great
inspiration from that tool. Given that the concept of the disease knowledge
workbench was very much exploratory, a way to develop a prototype at
minimal cost was essential. It was here that experience with SMW within
Targetpedia enabled the group to move rapidly and create an AlzSWAN-
like system, tailored to the IDU's needs, within a matter of weeks. Without
the ready availability of a technology that could provide the basis for the
system, it is unlikely that the idea of building the workbench would have
turned into a reality.
Modelling information in DKWB
Informaticians and IDU scientists collaborated in developing the
information model required to correctly manage disease information in
the workbench. The principle need was to take a condition such as sepsis
and divide it into individual physiological components, as shown in
Figure 17.7.
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