Semantic Web Interest Grows
Life science researchers are taking a closer look
at the potential benefits of adopting Semantic Web technology
to deal with their data handling and analysis challenges.
Discussion of the topic was one of the IT
highlights of last month's Bio-IT World Conference +
Expo.
World Wide Web Consortium (W3C) director Tim
Berners-Lee used his keynote address to encourage life
scientists to immediately start adopting a Semantic Web
approach with their data. He believes that life sciences can
benefit from Semantic Web technology, given the field's vast
array of disparate data types and formats and the increasing
need to approach problems in a multi-disciplinary way.
And several speakers in the three-day "IT
Solutions for Drug Discovery" conference track discussed their
efforts to adopt a Semantic Web approach to their data.
For instance, Tonya Hongsermeier, corporate
manager, clinical knowledge management and decision support at
Partners HealthCare System, and her colleague
Vipul Kashyap, senior medical informatician at Partners,
discussed some of the work the company is doing with Semantic
Web -based decision support systems.
"We need an actionable decision support system
that works in the context of [an organization's] workflow,"
said Hongsermeier.
Partners is using Semantic Web standards such as
the Resource Description Framework (RDF) to help make
electronic medical record (EMR) patient data such as age,
medical history, and family history available to computer
models. Having the data in RDF format allows Partners to use
what is called the Semantic Web Rules Language (SWRL) to write
decision support rules for treatments or selecting patients
for trials.
Partners can then use SWRL to set criteria for
using a particular diagnostic test. Specifically, using SWRL,
a complex if/then statement is created. For instance, one
combination of criteria and action might be: If the patient is
over 50 years old, has a family history of diabetes, and is
over a certain weight, order this test.
With the Semantic Web approach, the RDF information
easily identifies age, diabetes, and weight. This is not
unique to Semantic Web. In fact, most database applications
and standard SQL queries could do the same thing. However,
Kashyap noted that the power of the Semantic Web approach is
that the coding language is very concise, taking only a few
lines of code for complete queries. That said, he noted that
the real advantage of this approach is its flexibility. New
characteristics can be quickly added, and there is no need to
develop, in advance, a database that includes every possible
combination of patient data.
Along similar lines, Eric Neumann, global head of
knowledge management at Sanofi-Aventis, spoke about the use of
Semantic Web technology in drug discovery and development.
"There is a critical need to develop an informatics and
knowledge model across the drug [development] pipeline," said
Neumann. He noted that the traditional approach to accessing
and using data in applications has limitations. "IT tools and
APIs [application programming interfaces] are great if things
are constant," said Neumann.
But this is not the case with drug development.
"As applications become more complex, it is necessary to
include semantics into them," said Neumann. He noted that RDF
represents knowledge ("It's not just facts, but assertions,"
said Neumann) and that the Semantic Web approach leads to what
is called knowledge aggregation.
The difference between the regular Web and Semantic
Web technology is that more powerful techniques can be applied
to large amounts of data. "With the Semantic Web, you publish
meaning, not just data," said Neumann.
A fall issue of Bio-IT World will have
a feature about Semantic Web in the life sciences. What are
the key issues you'd like to know about in that article? Are
you adopting Semantic Web technology already? What do you see
as the obstacles to using Semantic Web? What benefits do you
hope to get from Semantic Web technology? Drop me a note at Salvatore_Salamone@bio-itworld.com
and tell me your views of this
technology.
Workflow Vendors Expand
Partnerships
Within the last month, data analysis pipelining and
computational workflow software vendors InforSense, SciTegic,
and TurboWorx separately announced new partnerships with key
third parties.
Frequently, partnerships are a major consideration
when life science organizations select pipelining and workflow
software. The reason is that a partnership typically ensures
that the third party's software or hardware tightly integrates
with the workflow or pipelining application. Read more.
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