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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 AnonTechie on Wednesday August 27 2014, @09:02AM
Here is some additional info about analyzing and processing BIG DATA.
Big Data presents scientists with unfolding opportunities, including, for instance, the possibility of discovering heterogeneous characteristics in the population leading to the development of personalized treatments and highly individualized services. But ever-expanding data sets introduce new challenges in terms of statistical analysis, bias sampling, computational costs, noise accumulation, spurious correlations, and measurement errors.
The era of Big Data – marked by a Big Bang-like explosion of information about everything from patterns of use of the World Wide Web to individual genomes – is being propelled by massive amounts of very high-dimensional or unstructured data, continuously produced and stored at a decreasing cost.
"In genomics we have seen a dramatic drop in price for whole genome sequencing," state Jianqing Fan and Han Liu, scientists at Princeton University, and Fang Han at Johns Hopkins. "This is also true in other areas such as social media analysis, biomedical imaging, high-frequency finance, analysis of surveillance videos and retail sales," they point out in a paper titled "Challenges of Big Data analysis" published in the Beijing-based journal National Science Review.
http://www.eurekalert.org/pub_releases/2014-08/scp-bbe082614.php [eurekalert.org]
[Paper]: http://nsr.oxfordjournals.org/content/1/2/293.full [oxfordjournals.org]
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