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With approximately 50 million scientific papers available in public databases– and a new one publishing nearly every 30 seconds – scientists cannot know about every relevant study when they are deciding where to take their research next.
A new tool in development by computational biologists at Baylor College of Medicine and analytics experts at IBM research and tested as a “proof-of-principle” may one day help researchers mine all public medical literature and formulate hypotheses that promise the greatest reward when pursuing new scientific studies.
Knowledge Integration Toolkit or KnIT. In a retrospective case study involving published data on p53, an important tumor suppressor protein, the team showed that this new resource called the Knowledge Integration Toolkit (KnIT) is an important first step in that direction, accurately predicting the existence of proteins that modify p53 – proteins that were subsequently found to do just that.
[Abstract]: http://dl.acm.org/citation.cfm?id=2623667
https://www.bcm.edu/news/research/automated-reasoning-hypothesis-generation
(Score: 2) by SlimmPickens on Wednesday August 27 2014, @07:30AM
One group make wicked spreadsheets, someone else uses a proprietary database and so on. There's sooo much data out there and yet you can't even have a little narrow AI look for correlation. Obviously this system is going to do that but who knows what "query the system" will end up meaning.