Seeing the big picture, not just vast amounts of data.
Now more than ever, there are powerful analytical tools available
that can parse large sets of genomic and other kinds of data. And
any good R&D organization has extraordinarily skilled computational biologists, bioinformaticians, and researchers on staff.
We're not competing with these resources - instead we're complementing and focusing them in a very unique way.
Think about it as a progression. The technical wizardry of computational biologists can create sophisticated algorithms to compile and curate information from large genomic and other types
of datasets - but what exactly is that information telling you?
And research scientists with deep, specific knowledge can characterize novel genes in vitro and in vivo to better understand
their function and validate hypotheses about their importance in disease. But do you know where to best focus their efforts, or are
you taking an indiscriminate sledgehammer approach - tackling as many targets as time and resources will permit?
Something's missing in the middle.
Something which is affecting your efficiency and the likelihood
that your R&D organization is on the right track to the next therapeutic breakthrough.
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