If we strip out emotion, one could say the main role of the human brain is to interpret massive amounts of data using sophisticated programming.
Add to that the inevitable explosive growth of internet connected "things" and the speed at which artificial intelligence is maturing to help interpret massive amounts of data and make decisions.
Computational processing, analyzing, and interpreting the massive amounts of data and genetic variants produced by NGS still remains challenging and requires comparisons with databases such as dbSNP and 1000 genomes project [ 3, 16].
These faster computers, in turn, are making it possible to design new forms of energy, smaller and more powerful sensors, artificial-intelligence software that can interpret the massive amounts of information that we are gathering, and robots that can do the mundane work of humans.
Upon these databases, a series of effective analysis tools were developed as well to explore and interpret the massive amounts of data in the context of existing knowledge.
The main challenge of NGS, however, lies in interpreting the massive amount of resulting data.
One strategy to address the challenges of interpreting the massive amount of data returned by WES has been to perform WES, but to initially restrict analysis to genes already known to be associated with the disease.
Despite these rapid advances, challenges remain in interpreting the massive amount of genomic variation data to 1) identify susceptibility genes associated with particular complex diseases and 2) understand the underlying etiology of the complex diseases.
BDAs are web-based applications that interpret and use massive amounts of enterprise and web-scale data to deliver more intelligent results for their subscribers.
