data published in Nature Biotechnology, represents the largest ever
analysis of immune cell signaling research, mapping more than 3,000
previously unlisted cellular interactions, and yielding the first ever
immune-centric modular classification of diseases. These data serve to
rewrite the reference book on immune-focused inter-cellular
communications and disease relationships.
The immune system is highly complex and dynamic, and with a new
immunology paper published every 30 minutes, there is no practical way
for a human to grapple with the sheer size and diversity of the field.
As this body of data grows, machine-learning methods will be the only
practical way of fully leveraging all the efforts being made to advance
immunology and science in general.
Standardizing and contextualising the full body of cell-cytokine
relationships is vital in our ability to broaden immune system
understanding. Based on this curated knowledge base, 355 hypotheses for
entirely novel cell-cytokine interactions were generated through the
application of validated prediction technologies.
These alone, represent discoveries born out of a better contextual
understanding of existing immune system knowledge. This potential
becomes even more powerful when such knowledge can be integrated with
other rich data sources and AI technologies to generate significant new
clues in the fight against disease.
Cell Talk – re-writing the book on immune-focused inter-cellular
“Given the dominant role the immune system plays in disease, an
immune-centric view takes us towards a better understanding of disease
mechanisms,” said Professor Shai Shen-Orr, PhD., Chief Scientist at
CytoReason and Director of Systems Immunology at the Technion. “These
data demonstrate that valuable, validated predictions are possible just
by mining and learning from existing papers. This ability grows
exponentially when you integrate it with other prediction technologies
and additional data sets.”
“This important piece of work changes the paradigm in what can be
predicted when you interfere with a particular receptor, molecule or
cell – specific to a disease or tissue. This work, combined with our
Cell-Centred Model, doesn’t just describe what happens between the cells
etc, but also defines who initiates and who acts on it – this is the key
to the uniquely 3-dimensional view of the immune system that CytoReason
Born out of 10 years’ research from Stanford and the Technion,
CytoReason is the only AI company to focus entirely on the immune system
in developing its proprietary data and AI / machine-learning approach.
This approach is capable of constructing 3-dimensional maps of
previously hidden, immune-system relationships at a cellular, tissue and
disease level. We call this the CytoReason Cell-Centered Model, and it
is the key to unlocking immune system relational insights that can lead
to biological discovery. Our cell-centered approach leverages public and
proprietary data, proprietary technologies and methodologies, which when
integrated with client data provides disease- and tissue-specific
insights that can enhance discovery, shorten trial phases and reduce
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