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In 2003,

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when we sequenced the human genome,

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we thought we would have the answer
to treat many diseases,

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but the reality is far from that,

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because in addition to our genes,

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our environment and lifestyle

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could have a significant role

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in developing many major diseases.

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One example is fatty liver disease,

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which is affecting over 20 percent
of the population globally

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and it has no treatment
and leads to liver cancer

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or liver failure.

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So sequencing DNA alone
doesn't give us enough information

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to find effective therapeutics.

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On the bright side, there are
many other molecules in our body.

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In fact, there are
over 100,000 metabolites.

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Metabolites are any molecule
that is super-small in their size.

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Known examples are glucose,
fructose, fats, cholesterol,

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things we hear all the time.

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Metabolites are involved
in our metabolism.

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They are also downstream of DNA,

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so they carry information
from both our genes

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as well as lifestyle.

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Understanding metabolites is essential
to find treatments for many diseases.

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I've always wanted to treat patients.

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Despite that, 15 years ago
I left medical school,

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as I missed mathematics.

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Soon after, I found the coolest thing:

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I can use mathematics to study medicine.

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Since then, I've been developing
algorithms to analyze biological data.

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So it sounded easy:

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let's collect data from
all the metabolites in our body,

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develop mathematical models to describe
how they are changed in a disease,

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and intervene those changes to treat them.

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Then I realized why no one
has done this before.

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It's extremely difficult.

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There are many metabolites in our body.

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Each one is different from the other one.

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For some metabolites,
we can measure their molecular mass

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using mass spectrometry instruments,

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but because there could be, like,
10 molecules with the exact same mass,

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we don't know exactly what they are,

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and if you want to clearly
identify all of them,

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you have to do more experiments,
which could take decades

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and billions of dollars.

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So we developed
an artificial intelligence,

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or AI, platform to do that.

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We leveraged the growth of biological data

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and built a database of any existing
information about metabolites

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and their interactions
with other molecules.

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We combined all this data
as a mega-network.

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then, from tissues or blood of patients,

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we measured masses of metabolites

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and find the masses
that are changed in a disease.

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But, as I mentioned earlier,
we don't know exactly what they are.

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Like a molecular mass of 180 could be
the glucose, galactose, or fructose.

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They all have the exact same mass,

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but different functions in our body.

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Our AI algorithm considered
all these ambiguities.

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It then mined that mega-network

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to find how those metabolic masses
are connected to each other

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that result in disease,

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and because of the way they are connected,

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then we are able to infer
what each metabolite mass is,

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like that 180 could be glucose here,

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and more importantly to discover
how changes in glucose

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and other metabolites

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lead to a disease.

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This novel understanding
of disease mechanisms

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then enable us to discover
effective therapeutics to target that.

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So we formed a startup company
to bring this technology to the market

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and impacts people's lives.

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Now, my team and I at ReviveMed
are working to discover

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therapeutics for major diseases
that metabolites are key drivers for,

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like fatty liver disease,

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because it is caused
by accumulation of fats,

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which are types of metabolites,

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in the liver,

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and as I mentioned earlier
is a huge epidemic with no treatment.

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And fatty liver disease
is just one example.

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Moving forward, we are going to tackle
hundreds of other diseases

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with no treatment,

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and by collecting more and more
data about metabolites

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and understanding
how changes in metabolites

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leads to developing diseases,

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our algorithms will get
smarter and smarter

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to discover the right therapeutics
for the right patients,

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and we will get closer to reach our vision

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of saving lives with every line of code.

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Thank you.

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(Applause)