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systems thinking principles to complex biological or clinical problems) is
beginning to demonstrate that by taking a systems level approach, which is now
possible given new and extremely high-capacity instrumentation and data man-
agement tools not previously available, we can in fact study complex systems as
a whole. These systems-approaches allow for the realization of benefi ts related
to network-level analyses of the interactions between important entities in a bio-
logical or disease system and their role in high impact end points such as iden-
tifying new uses of existing drugs, identifying important markers for risk of
disease; or developing novel therapeutic strategies for a broad spectrum of
pathophysiological states.
Thus, when examined in a similar systems level perspective, prevailing
approaches to the pursuit of biomedical and healthcare research are showing signs
of moving from a historically motivated tendency for reductionism, towards a sys-
tems thinking model, with all of the aforementioned and resultant potential
benefi ts.
1.2.3
Towards a Central Dogma for Biomedical Informatics
Finally, in a manner that is crosscutting and underlies the role of Biomedical
Informatics as it pertains to both translational science and systems thinking, the
increasing maturity of the fi eld is leading to the recognition of a “working” central
dogma for Biomedical Informatics. In this “working” defi nition of the broad pur-
pose for Biomedical Informatics as a scientifi c fi eld, core theories and methods that
span the discipline that can collectively be seen as contributing to the translation of
raw data into information through the provision of context and subsequently the
translation of such information into knowledge by rendering it in a manner that is
actionable. For example, given a clinical data point such as a laboratory value,
through the addition of metadata (e.g., context) via the use of technical standards
and knowledge engineering methods, we are able to transform that data into infor-
mation. Subsequently, by representing and communicating that information to a
clinical decision support system that has been designed and validated using decision
modeling and analysis methods and frameworks, we can render it actionable as
knowledge for clinical decision making at the point-of-care. As another example,
given a transcriptome sequencing dataset, we can normalize and structurally and/or
functionally annotate up- or down-regulated genes using available knowledge bases
and deep semantic reasoning methods so as to create an information resource that
characterizes a given sample. We could then apply advanced data visualization and
interactive data analytic frameworks and methods to deliver a graphical representa-
tion of such an information resource. Investigators can then identify and refi ne pat-
terns or motifs of interest relative to their experimental paradigm, thus rendering the
underlying information actionable as a knowledge resource. This overall working
“central dogma” is illustrated in Fig.
1.3
.
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