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text mining

Ramneek Gupta ramneek at cbs.dtu.dk
Thu May 16 06:34:32 PDT 2002



Perhaps one of the things you're aiming to build is a
substitution matrix for ontology terms (using e.g.
evolutionarily related or sequence related proteins).
Of course, assuming that this isn't the way GO terms
are associated to the proteins in the first place..

Or am I completely off-base making no sense at all ?
(could very well be !)


.. Ramneek


Ramneek Gupta
Centre for Biological Sequence Analysis
BioCentrum, Bldg-208                    Tel: +45-4525-2472
Technical University of Denmark         Fax: +45-4593-1585
Lyngby, DK-2800, Denmark                ramneek at cbs.dtu.dk

On Thu, 16 May 2002, andrew brian clegg wrote:

>
>
> On the subject of text mining...
>
> I'm embarking on a project to build a smart search engine for biological
> literature with Gene Ontology (and its cross-references to other
> databases) as the central framework.
>
> The initial goal is to allow users to enter search terms, and be presented
> with a scored list of fuzzy hits, where a document matching a search term
> exactly scores highly, and a document matching a GO term closely related
> to a search term scores less highly, with a weighting based on closeness
> in ontology, direction and class of relationship, etc.
>
> I hope to extend this to cover clustering of documents around terms,
> detection of similar themes across sets of documents, and eventually more
> sophisticated discovery of connections between themes, hopefully leading
> to a rudimentary assault on the knowledge-discovery problem.
>
> Does anyone have any experiences or ideas they would like to relate? I'm
> particularly keen to hear of any false starts to be wary of or wrong trees
> to avoid barking up. There's a lot of literature around on the subject of
> information retrieval and text mining but I've found little so far that
> covers the use of ontologies -- apart from for special purposes like
> disambiguation of terms. Also the problem of indexing a document in terms
> of its relationship to a set or structure of terms -- without the multi-
> dimensionality involved getting out of control -- is going to prove
> interesting, I'm sure.
>
> (Actually if anyone knows of any papers or textbooks that cover
> efficient ways to index a document against a flat list of terms, I'd
> love to hear of them. I'm sure that kind of approach could be adapted
> easily.)
>
> Thanks in advance for any pointers.
>
> Andrew Clegg
> School of Crystallography
> Birkbeck, University of London
>
>
> On Wed, 15 May 2002, C. E. Crangle wrote:
>
> > We are also interested in this question, with a focus on text data mining
> > applications.
> >
> > C. E. Crangle, Ph.D.
> > Senior Partner, ConverSpeech LLC
> > www.converspeech.com
> > Tel: (001)1-650-322-9257
> > Fax: (001)1-650-328-6138
> >
> > > Second: Do you know of any attempt to map between (or even
> > > unify) the GO ontologies, and the 'orthology' used by KEGG?
> > > Here at the Institute of Systems Biology, our biologists are
> > > very fond of KEGG; I -- as a programmer -- am naturally
> > > drawn to clean design of GO, which I have used for the last year.
>
> --
>
>
>
> --
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