Overcoming the Challenges of Text-Mining in Biotech Research

01/03/2016 - 4 minutes

A few weeks ago we introduced you to the concept of text-mining in Biotech through this article. Now that you’re more familiar with the concept, let’s ‘mine’ a bit into the current challenges in this field and how to overcome them.


Data produced through Biotech research grows with an incredible rate. Everyday, scientists are finding new links between molecules, diseases or life mechanisms. This ‘Big Data’ phenomenon can become overwhelming for researchers who are searching for a precise answer. That’s where text-mining can be helpful. Specific algorithms behind this technology are capable of sifting through huge amounts of data to find the specific answer to an unanswered question.

To show you how powerful it could be, just look at the example below where we asked a text-mining tool to show us which genes are associated with breast cancer:

Try to run the same inquiry in a standard database such as PubMed and you will get a long list of results, which you will have to read and analyze to find any relevant answer… So no doubt,

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