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Mining biotech's data mother lode

The BioGrid project brought together six partners from the UK, Germany, Cyprus and The Netherlands to address one of the key problems facing the life sciences today.

"How to integrate the huge volume of disparate data ?on gene expression, protein interactions and the vast output of literature both inside and outside laboratories ?to find out what is important," says Dr Michael Schroeder, Professor of the Bioinformatics group at Dresden Technical University and coordinator of this IST-funded project. "I attended a workshop recently, held by the W3 consortium, and many of the companies there said that this was the biggest problem they face."

Currently, pharmaceutical and biotech companies produce vast quantities of raw data on the problems that interests them. Microarrays process thousands of samples to discover what genes are over expressing. These over-expressing genes ?numbering sometimes in their thousands, too ?create proteins. The researchers then need to discover what protein interactions are taking place among all the different proteins created by the over-expressing genes. This is not trivial.

If a researcher can identify protein interactions they then need to do a search on their company intranet to see what other work company labs have produced relevant to the topic. Finally, the researcher must perform a search of academic journals to find relevant journal papers. Currently PubMed, the most important public literature database available, has 15,000,000 entries, and the number is growing every day. Finding relevant data there is again not a trivial task.

Dr Schroeder gives an example. "The medical faculty here were studying pancreatic tumours. They found 1,000 genes over expressing. Using our software they were able to find, among others, three protein interactions that were particularly relevant. Using our literature search ontology they were able to discover that two of these interactions were novel. They are now going to s
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