SWAT4LS2009 – Michael Schroeder: Predicton of Drug Target Interactions from Literature by Context Similarity

Typical researcher spends 12.4 hours a week searching for information. Why not use Google? ‘Cause Google is not semantic.

Go PubMed – Filter PubMed contents against all the terms in the Gene Ontology. If you use simple categorisation for information retrieval potentially increase search burden due to compartmentalisation. However works the other way round too…useful filtering.

Showing some examples of faceted browsing of PubMed content and systematic drilldown into search results. Not easy to blog, but literature exploration in this way is always fascinating. Examples include the analysis of research trends, networks of colaborators etc..new tool in Go PubMed also allows the discovery of indirect links or inferred links.

Have developed a similar system for the web: Go Web (works on the top yahoo search results).

Remarks on Ontology Generation: have developed a plugin for OBO Edit…search for term and plugin makes suggestions for terms that might be included in new ontologies. Points out terms in existing ontologies. Also helps with the generation of definitions for terms…wow this is extremely useful in SO many ways….

Now let’s talk about drugs and targets….

Try and mine for gene mentions in text…find a gene term and then use context to decide what it is we are talking about. Once gene has found look for statistically significant co-occurences. The results have been made available in GoGene. Again can do bibliometric trend analysis – genes are ranked by community interest.

From drugs to genes..what is the link between a gene and a drug using context profiles: what are the disease terms related to a given drug…then to genes.

Gotta stop blogging…enjoying this talk far too much…….

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