Re: An application of the Semantic Web for finding alternative drug applications

Amit,
There are a large class of data discovery problems that cannot be solved via
(SPARQL) query or even inferencing, simply because we don't know what
precise questions to ask in advance, though we may already have enough
evidence at hand (stored).

These kinds of problems (limited associative models) lend themselves more to
applications of large-scale statistical mining and bayesian modeling...
However, the latter tools are usually applied when one is looking at 1-4
parameters at a time (e.g., incidence of a disease is dependent on genetic
factors, age, and diet). Now with the possibility of having hundreds of such
different attributes available semantically, statistical approaches will
have to be augmented greatly.

SPARQL is an essential component for accessing/constraining SW data, but by
itself it is insufficient for the discovery of new associations and
mechanisms in whole biological systems. Many of the major
challenges within pharma R&D are precisely of this type!

cheers,
Eric


On Thu, Sep 11, 2008 at 1:24 PM, Amit Sheth <amitpsheth@gmail.com> wrote:

> Finding "potentially interesting" paths, subgraphs, and pattering in
> semantic web data (eg those
> created from complex entity and relationship extraction from biomedical
> literature [1],
> semantic annotation and provenence of experimental data, and of course
> structured datatabases) is
> very useful in biomedical research
> and requires SPARQL extensions. One of several examples along this line is
> the
> support for path queries as in SPARQ2L [2]. Other interesting examples are
> supporting spatio-temporal thematic queries and corresponding extensions
> such as SPARQ-ST
> [3] albeit we have not applied these extensions to sensor data so far and
> not (yet) to biomedical domain.
>
> Amit <http://knoesis.org/amit>
>
> [1]
> http://knoesis.wright.edu/research/semweb/projects/textMining/ekaw2008/
> [2] http://knoesis.wright.edu/library/resource.php?id=00060
> [3] http://knoesis.org/research/semweb/projects/stt/
>
>
>
>
> On Thu, Sep 11, 2008 at 10:50 AM, Kei Cheung <kei.cheung@yale.edu> wrote:
>
>>
>> Peter Ansell wrote:
>>
>>> ----- "Kei Cheung" <kei.cheung@yale.edu> wrote:
>>>
>>>
>>>
>>>> From: "Kei Cheung" <kei.cheung@yale.edu>
>>>> To: "eric neumann" <ekneumann@gmail.com>
>>>> Cc: "w3c semweb hcls" <public-semweb-lifesci@w3.org>
>>>> Sent: Thursday, September 11, 2008 6:42:33 AM GMT +10:00 Brisbane
>>>> Subject: Re: An application of the Semantic Web for finding alternative
>>>> drug  applications
>>>>
>>>> Thanks for sharing the papers, Eric. I went through some of the papers
>>>> including the one you mentioned (interestingly there is a paper on
>>>> wiki). I think they're interesting. They reminded me of "mining for the
>>>> semantic web" (ontology learning?) and "mining from the semantic web"
>>>> (data mining). For biological networks, we need to do both semantic and
>>>> topological queries. It might be difficult to achieve the latter using
>>>> SPARQL (e.g., finding protein hubs). Maybe we need some extensions of
>>>> SPARQL.
>>>>
>>>> Best,
>>>>
>>>> -Kei
>>>>
>>>>
>>>
>>> What are the limits to what you can do with bare SPARQL in this area?
>>> Does it help to have elementary rdfs subclass knowledge for the topological
>>> parts?
>>>
>>> Cheers,
>>>
>>> Peter
>>>
>>>
>>>
>> Hi Peter,
>>
>> When YeastHub [1] was being built, I was wondering whether Semantic Web
>> (SW) technologies can help facilitate integrative biological network
>> analysis including network topology. Later, a web-based tool called "tYNA"
>> was created and published [2] which supports biological network
>> analysis/visualization. tYNA was not implemented using SW, but I still
>> wonder how some of its features can be implemented using SW.
>>
>> [1] http://bioinformatics.oxfordjournals.org/cgi/reprint/21/suppl_1/i85
>> [2] http://bioinformatics.oxfordjournals.org/cgi/content/full/22/23/2968
>>
>> Cheers,
>>
>> -Kei
>>
>>
>>
>>
>
>
> --
> Amit Sheth http://knoesis.org
>