Biomedical Engineering Reference
In-Depth Information
course adds a new dimension to knowledge sharing not available in traditional
publishing media.
The platform also provides access to an ecosystem and catalog of core
services and capabilities. Individual researchers, institutions, and companies
can publish information about their core competencies and resources, from
clinical trial design to molecular profi ling, and (where possible) computational
interfaces to these services (commonly known as application programming
interfaces, or APIs). The operators of personalized virtual biotechs can readily
discover and use these APIs through the platform, thus offering participating
researchers unprecedented access to industrial-scale, high-quality services.
Workfl ow planning, execution, and tracking, mentioned above (although not
yet implemented in the current Cancer Commons platform), go hand-in-hand
with this service's ecosystem. When workfl ow facilities become available, they
will enable operators of personalized virtual biotechs to automate and share
best practices.
A typical complex workfl ow might continuously search connected specimen
banks for relevant newly deposited materials, automatically dispatch them for
molecular profi ling, gather and reintegrate the resulting data, run it through
statistical methods that correlate the molecular with clinical data related to
the specimens, and make the results available for collaborative interpretation
and decision making. When a decision is made regarding hypotheses to move
forward, an adaptive N -of-1 experiment, such as described in the preceding
section, can be planned and operated through the platform, and the data can
be gathered and analyzed in the same setting.
The sheer volume and complexity of available information—genes, pro-
teins, pathways, disease, mechanisms, drugs, patient clinical, genomic, and
response profi les, and so forth—far exceed the synthetic and analytic capaci-
ties of any individual or human analysts. To create understanding from infor-
mation, we need biocomputational tools that can manipulate, check, and use
the formal representations to make predictions and form explanations. Such
tools for hypothesis generation, knowledge capture, and so forth, use formal
representation and computational methods to enable scientists to work effi -
ciently with complex models [10]. By virtue of having been built on top of
BioBike, a powerful cloud-based semantic biocomputation platform [3], the
Cancer Commons platform provides access to state- of - the - art computational
biology and machine learning services and integrated access to a variety of
data and knowledge from databases that contain curated representations of
clinical trials, known regulatory pathways, drug targeting information, genomic,
proteomic, and biochemical knowledge, and so forth, as needed by the opera-
tions of the various computations carried out through the platform.
Taken in combination, the facilities of Cancer Commons offer its
community of patients, physicians, and researchers unprecedented power to
collaboratively manipulate and interpret the wealth of data and knowledge
contained within the commons, and from other (mostly public) resources, to
effi ciently search for treatments and cures.
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