As researchers team up with computer scientists to develop powerful algorithms and machine learning tools, they are increasing their capacity to identify patterns in
huge datasets of biological information and reveal unknown connections to human disease.
The research team, supervised by Dr Yorgos Tzimiropoulos, trained a CNN on
a huge dataset of 2D pictures and 3D facial models.
Not exact matches
«With large present - day genomic
datasets and increased international collaboration to handle the many newly sequenced ancient
datasets, there is
huge potential to understand the biology
of human prehistory in a way that has never been accessible before.»
«You can analyze
huge datasets in an instant and experiment with the fast - evolving world
of open source bioinformatics software as well as the vast amount
of publicly available data from previous studies.»
«Using the 5D colorimetric technique, these
huge datasets are transformed into a series
of color - coded dynamic patterns that actually reveal the neural choreography completely,» said Kelso.
«Technology wise, being able to handle such a
huge amount
of data and apply an advanced machine - learning algorithm was a big challenge before, but now we have supercomputers and the skills to handle the
dataset.»
Real - world scientific problems people face today involve
huge datasets that are only surmountable with the help
of computers.
But now that revelation has become a revolution in which companies, investors and policymakers use analysis
of huge datasets to discover empirical correlations between seemingly unrelated things.
The «Spaghetti graphs» in the following gives an impression
of the
huge variability among the
datasets.
The global HadCRUT4
dataset, updated through July 31, 2013, reveals little warming over 15 years despite the
huge influx
of human CO2 emissions and the subsequent large growth in atmospheric CO2 levels
He is what I would call an «expert» as he has had a
huge influence on the development
of reanalysis
datasets, old and new.
; — RRB - But I think it would be great if other groups explore these
huge datasets from CPDN, I know there's a nice portal one
of my colleagues at Oxford set up (Milo Thurston) to this end.
You then asked «Or perhaps you can point me to the
dataset that shows, for several individual locations for the same period as the temperature set the: * CO2 concentrations (OK, we could use Mauna Loa for that) * Aerosols (sorry, can't use global records for that, there can be
huge differences on a local scale) * Absolute humidity * TSI with correction for local albedo, including cloud albedo, and the place on earth» Well actually, I can and have for the USA in terms
of CO2, humidity (RH but AH also if you insist), and albedo, not to mention actual solar surface radiation, and various other variables (eg windspeed), as I have previously reported here for quite a few locations, eg Pt Barrow.
Still, Funk's model was a big advance: It allowed the technique to work well with
huge data sets, even those with lots
of missing data — like the Netflix
dataset, where a typical user rated only few dozen films out
of the thousands in the company's library.
While it's been nothing short
of a
huge undertaking, we've developed the deepest
dataset in the industry to provide insights not previously possible.