Life Sciences model helps scientists
make faster and better decisions by
combining structured data retrieval,
literature search, and scientific
analysis into a repeatable workflow in
Codex. In this instance, the Life
Sciences model is [music] asked to
compare and prioritize three targets:
IL-33,
TSLP, and IL-1 RA1 for asthma. It starts
with an internal evidence package, which
includes internal assay results,
biomarker strategy, tractability and
safety,
as well as a target product profile.
The Life Sciences model gives a crisp,
top-line recommendation.
It ranks the [music] targets
and grounds the recommendation based on
the local data files found.
Codex also implies that there's an
opportunity to expand upon [music]
additional evidence around human
genetics or target disease evidence.
The Life Sciences model can also
leverage the Life Sciences [music]
research plugin to pull in additional
relevant evidence.
The model has been trained on when best
to invoke appropriate Life Sciences
skills, as well as how to synthesize the
outputs of those skills.
Let's also leverage the power of Codex
to spawn sub-agents to tackle [music]
each lane of evidence.
That keeps the genetics, translational
biology, regulatory context, and other
criteria separate and unbiased [music]
until the final synthesis.
The Life Sciences model has directed
Pascal to be responsible for all of the
human genetics evidence that should be
leveraged in comparing [music] the three
asthma targets. It correctly outlines a
list of skills that are relevant in
order to pull in the right level of
human genetic evidence. Once all of the
six agents that have been sub-spawned
have completed [music] their output,
the total outputs will be synthesized in
order to generate a final
prioritization.
By leveraging the output from a variety
of databases, the model is able to
surface locus-to-gene context, follow
signals across cohorts, bring in target
disease evidence, and literature to help
resolve ambiguity.
>> [music]
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>> The Life Sciences model is primed with
greater thinking and bio-intelligence
for [music] complex scientific tasks.
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