Notes from the Exhibit Floor: the way cool Biovista Notes from the Exhibit Floor:  the way cool Biovista

This is what I’m talking about – uses of the STM literature and the capabilities of the NIH databases to visualize information in new ways and make new connections and new science discoveries.  To get the unexpected connections you can’t get through standard search.  If you are in bioinformatics, you should probably already know about this product.  One of the presenters at DASER really gave us a great pep talk on bioinformatics and how librarians can help (I’m writing this offline in another session, so not able to link or look this up :(  ) but I think this tool actually makes it doable while still using the valuable NIH information tools.

I probably talked with Drs. Aris and Andreas Persidis for an hour – totally monopolizing their time and keeping me from running home and working on that paper (well and the other one, and the third one…)

The current tool uses medline, genebank, and other databases and does visualization using Y-graph.  The facets (or buckets or bins) appear on the right or if you right click, you can see the strength of the ties between items and you can show the commonalities on many other facets.  I’m  having a hard time explaining it now but you search a general topic – like a type of cancer.  Then (something I understand) you can map the authors and see which two authors, three authors, etc., both looked at using the same drug to treat the same problem… Any bioinformaticians in the house?  Another thing does a Bayesian thing that helps you to find analogs on the various facets (find another disease that attacks the same thing that can be fought with x drug…)

They gave me a couple of white papers which talk a little more about this.  One of them is a reprint from what looks like a journal article (I’m offline right now so don’t know anything about this journal) A. Persidis, S. Deftereos, and A. Persidis. (2004) Systems literature analysis.  Pharmacogenetics 5, 7, 943-947.  A quote:

A basic tenet of systems biology is the need to examine biology in terms of the dynamic structure and inter-relationships of all the components of a call or organism, and not the individual constituents in isolation.  By analogy, SLA treats large sets of scientific literature as a system of millions of interconnections between research parameters, such as genes, diseases, tissues, cell events, model organisms, and experiment types.

They’re also looking at other subject areas besides biochemistry to apply this work.
CIL2006  ¶ 8:27 PM| |cites (technorati) |