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In 2003,
when we sequenced the human genome,
we thought we would have the answer to treat many diseases.
But the reality is far from that,
because in addition to our genes,
our environment and lifestyle could have a significant role
in developing many major diseases.
One example is fatty liver disease,
which is affecting over 20 percent of the population globally,
and it has no treatment and leads to liver cancer
or liver failure.
So sequencing DNA alone doesn't give us enough information
to find effective therapeutics.
On the bright side, there are many other molecules in our body.
In fact, there are over 100,000 metabolites.
Metabolites are any molecule that is supersmall in their size.
Known examples are glucose, fructose, fats, cholesterol --
things we hear all the time.
Metabolites are involved in our metabolism.
They are also downstream of DNA,
so they carry information from both our genes as well as lifestyle.
Understanding metabolites is essential to find treatments for many diseases.
I've always wanted to treat patients.
Despite that, 15 years ago, I left medical school,
as I missed mathematics.
Soon after, I found the coolest thing:
I can use mathematics to study medicine.
Since then, I've been developing algorithms to analyze biological data.
So, it sounded easy:
let's collect data from all the metabolites in our body,
develop mathematical models to describe how they are changed in a disease
and intervene in those changes to treat them.
Then I realized why no one has done this before:
it's extremely difficult.
(Laughter)
There are many metabolites in our body.
Each one is different from the other one.
For some metabolites, we can measure their molecular mass
using mass spectrometry instruments.
But because there could be, like, 10 molecules with the exact same mass,
we don't know exactly what they are,
and if you want to clearly identify all of them,
you have to do more experiments, which could take decades
and billions of dollars.
So we developed an artificial intelligence, or AI, platform, to do that.
We leveraged the growth of biological data
and built a database of any existing information about metabolites
and their interactions with other molecules.
We combined all this data as a meganetwork.
Then, from tissues or blood of patients,
we measure masses of metabolites
and find the masses that are changed in a disease.
But, as I mentioned earlier, we don't know exactly what they are.
A molecular mass of 180 could be either the glucose, galactose or fructose.
They all have the exact same mass
but different functions in our body.
Our AI algorithm considered all these ambiguities.
It then mined that meganetwork
to find how those metabolic masses are connected to each other
that result in disease.
And because of the way they are connected,
then we are able to infer what each metabolite mass is,
like that 180 could be glucose here,
and, more importantly, to discover
how changes in glucose and other metabolites
lead to a disease.
This novel understanding of disease mechanisms
then enable us to discover effective therapeutics to target that.
So we formed a start-up company to bring this technology to the market
and impact people's lives.
Now my team and I at ReviveMed are working to discover
therapeutics for major diseases that metabolites are key drivers for,
like fatty liver disease,
because it is caused by accumulation of fats,
which are types of metabolites in the liver.
As I mentioned earlier, it's a huge epidemic with no treatment.
And fatty liver disease is just one example.
Moving forward, we are going to tackle hundreds of other diseases
with no treatment.
And by collecting more and more data about metabolites
and understanding how changes in metabolites
leads to developing diseases,
our algorithms will get smarter and smarter
to discover the right therapeutics for the right patients.
And we will get closer to reach our vision
of saving lives with every line of code.
Thank you.
(Applause)
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【TED】Leila Pirhaji: The medical potential of AI and metabolites (The medical potential of AI and metabolites | Leila Pirhaji)

191 Folder Collection
林宜悉 published on November 21, 2019
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