say you’re watching the Super Bowl and you see an ad for a prescription drug
you see an ad for a prescription drug
you see an ad for a prescription drug we’ve all seen them the beginning paints
we’ve all seen them the beginning paints
we’ve all seen them the beginning paints an optimistic picture of the drugs
an optimistic picture of the drugs
an optimistic picture of the drugs incredible curing capabilities followed
incredible curing capabilities followed
incredible curing capabilities followed by a lengthy list of side effects
by a lengthy list of side effects
by a lengthy list of side effects ranging from dizziness to death when
ranging from dizziness to death when
ranging from dizziness to death when someone takes a prescription drug
someone takes a prescription drug
someone takes a prescription drug they’re also taking a potentially fatal
they’re also taking a potentially fatal
they’re also taking a potentially fatal gamble with over 70% of Americans on at
gamble with over 70% of Americans on at
gamble with over 70% of Americans on at least one prescription variable
least one prescription variable
least one prescription variable responses to medication are a huge
responses to medication are a huge
responses to medication are a huge problem adverse drug events are
problem adverse drug events are
problem adverse drug events are responsible for approximately 50% of all
responsible for approximately 50% of all
responsible for approximately 50% of all annual Hospital all annual
annual Hospital all annual
annual Hospital all annual hospitalizations and are the fourth
hospitalizations and are the fourth
hospitalizations and are the fourth leading cause of death in the u.s.
leading cause of death in the u.s.
leading cause of death in the u.s. adverse drug events are responsible for
adverse drug events are responsible for
adverse drug events are responsible for approximately 1.5 million annual
approximately 1.5 million annual
approximately 1.5 million annual hospitalizations that’s 3 percent of all
hospitalizations that’s 3 percent of all
hospitalizations that’s 3 percent of all annual hospitalizations meaning that
annual hospitalizations meaning that
annual hospitalizations meaning that every minute three people are
every minute three people are
every minute three people are hospitalized with an adverse drug
hospitalized with an adverse drug
hospitalized with an adverse drug reaction why might a drug cure one
reaction why might a drug cure one
reaction why might a drug cure one person but poison another I decided to
person but poison another I decided to
person but poison another I decided to pursue a project in bioinformatics the
pursue a project in bioinformatics the
pursue a project in bioinformatics the science of analyzing genetic data to
science of analyzing genetic data to
science of analyzing genetic data to explore this question i hypothesized
explore this question i hypothesized
explore this question i hypothesized that a person’s response to a drug is
that a person’s response to a drug is
that a person’s response to a drug is influenced by their genome or all their
influenced by their genome or all their
influenced by their genome or all their DNA to develop my idea
DNA to develop my idea
DNA to develop my idea I thought about DNA and how drugs work
I thought about DNA and how drugs work
I thought about DNA and how drugs work what’s a genetics have to do with a
what’s a genetics have to do with a
what’s a genetics have to do with a drugs response once ingested the
drugs response once ingested the
drugs response once ingested the digestive system breaks down a drug into
digestive system breaks down a drug into
digestive system breaks down a drug into molecules that flow through the
molecules that flow through the
molecules that flow through the bloodstream a person responds to a drug
bloodstream a person responds to a drug
bloodstream a person responds to a drug when drug molecules bind to body
when drug molecules bind to body
when drug molecules bind to body molecules and trigger a certain response
molecules and trigger a certain response
molecules and trigger a certain response this response procedure is known as a
this response procedure is known as a
this response procedure is known as a self signaling pathway cell signaling
self signaling pathway cell signaling
self signaling pathway cell signaling pathways are like locking key mechanisms
pathways are like locking key mechanisms
pathways are like locking key mechanisms where the part of the drug molecule is
where the part of the drug molecule is
where the part of the drug molecule is the key that fits into part of the body
the key that fits into part of the body
the key that fits into part of the body molecule when molecules bind together
molecule when molecules bind together
molecule when molecules bind together they unlock the door to a physical
they unlock the door to a physical
they unlock the door to a physical response to a drug by sending a signal
response to a drug by sending a signal
response to a drug by sending a signal to the body chemicals in the body called
to the body chemicals in the body called
to the body chemicals in the body called enzymes speed up the reactions between
enzymes speed up the reactions between
enzymes speed up the reactions between drug molecules and body molecules and
drug molecules and body molecules and
drug molecules and body molecules and can therefore affect the cell signaling
can therefore affect the cell signaling
can therefore affect the cell signaling pathway DNA contains the instructions
pathway DNA contains the instructions
pathway DNA contains the instructions for the body to produce enzymes enzymes
for the body to produce enzymes enzymes
for the body to produce enzymes enzymes are made of proteins and your unique set
are made of proteins and your unique set
are made of proteins and your unique set of proteins is what makes you you
of proteins is what makes you you
of proteins is what makes you you mutations or changes in the DNA code
mutations or changes in the DNA code
mutations or changes in the DNA code could change what proteins and enzymes
could change what proteins and enzymes
could change what proteins and enzymes are produced therefore a change in the
are produced therefore a change in the
are produced therefore a change in the enzyme could change a signal that the
enzyme could change a signal that the
enzyme could change a signal that the drug sends to the body so enzymes maybe
drug sends to the body so enzymes maybe
drug sends to the body so enzymes maybe it was in mutations may be responsible
it was in mutations may be responsible
it was in mutations may be responsible for side effects when I read about
for side effects when I read about
for side effects when I read about genome sequencing I thought wow what if
genome sequencing I thought wow what if
genome sequencing I thought wow what if there’s a way to analyze a genome to
there’s a way to analyze a genome to
there’s a way to analyze a genome to find a mutation then there might be a
find a mutation then there might be a
find a mutation then there might be a way to correlate these mutations to side
way to correlate these mutations to side
way to correlate these mutations to side effects I researched mutations in
effects I researched mutations in
effects I researched mutations in enzymes critical to the absorption of
enzymes critical to the absorption of
enzymes critical to the absorption of drugs lipitor plavix and tegretol I
drugs lipitor plavix and tegretol I
drugs lipitor plavix and tegretol I focused on these drugs because they have
focused on these drugs because they have
focused on these drugs because they have been well studied using public databases
been well studied using public databases
been well studied using public databases I found mutations linked to drug in
I found mutations linked to drug in
I found mutations linked to drug in efficacy and side effects such as
efficacy and side effects such as
efficacy and side effects such as excessive bleeding for lipitor plavix
excessive bleeding for lipitor plavix
excessive bleeding for lipitor plavix and tegretol
however knowing the mutation only solves
however knowing the mutation only solves half the problem DNA code is composed of
half the problem DNA code is composed of
half the problem DNA code is composed of building blocks known as basis these
building blocks known as basis these
building blocks known as basis these bases are like the alphabet of the DNA
bases are like the alphabet of the DNA
bases are like the alphabet of the DNA code for proteins to know if there is an
code for proteins to know if there is an
code for proteins to know if there is an abnormal mutation in this DNA code
abnormal mutation in this DNA code
abnormal mutation in this DNA code doctors need to search the whole genome
doctors need to search the whole genome
doctors need to search the whole genome that’s six billion bases long when I saw
that’s six billion bases long when I saw
that’s six billion bases long when I saw this challenge I added to my hypothesis
this challenge I added to my hypothesis
this challenge I added to my hypothesis and predicted that I could innovate a
and predicted that I could innovate a
and predicted that I could innovate a computer algorithm or a problem solving
computer algorithm or a problem solving
computer algorithm or a problem solving procedure to find a mutation this
procedure to find a mutation this
procedure to find a mutation this sparked my next idea I could innovate a
sparked my next idea I could innovate a
sparked my next idea I could innovate a string searching algorithm string
string searching algorithm string
string searching algorithm string searching algorithms are computer
searching algorithms are computer
searching algorithms are computer programs that find a string of letters
programs that find a string of letters
programs that find a string of letters in a text the genome is the text and the
in a text the genome is the text and the
in a text the genome is the text and the mutation is the string how will string
mutation is the string how will string
mutation is the string how will string searching algorithms save lives these
searching algorithms save lives these
searching algorithms save lives these algorithms can find mutations allowing
algorithms can find mutations allowing
algorithms can find mutations allowing doctors to search a database for
doctors to search a database for
doctors to search a database for mutations compare them with ones known
mutations compare them with ones known
mutations compare them with ones known to cause side-effects and prescribe a
to cause side-effects and prescribe a
to cause side-effects and prescribe a possibly life-saving drug my innovation
possibly life-saving drug my innovation
possibly life-saving drug my innovation is a string searching algorithm that
is a string searching algorithm that
is a string searching algorithm that finds mutations linked to drug responses
finds mutations linked to drug responses
finds mutations linked to drug responses this can help solve the problem of
this can help solve the problem of
this can help solve the problem of variable responses to prescription drugs
variable responses to prescription drugs
variable responses to prescription drugs I create a design criteria for my
I create a design criteria for my
I create a design criteria for my innovation it needs to be functional
innovation it needs to be functional
innovation it needs to be functional feasible and fast why is there a need
feasible and fast why is there a need
feasible and fast why is there a need for speed a fast algorithm could be
for speed a fast algorithm could be
for speed a fast algorithm could be life-saving in emergency situations such
life-saving in emergency situations such
life-saving in emergency situations such as seizure or allergic shock when the
as seizure or allergic shock when the
as seizure or allergic shock when the correct drug must be prescribed
correct drug must be prescribed
correct drug must be prescribed immediately to address this need I
immediately to address this need I
immediately to address this need I tested several algorithms and narrowed
tested several algorithms and narrowed
tested several algorithms and narrowed it down to to apostille eco Giancarlo
it down to to apostille eco Giancarlo
it down to to apostille eco Giancarlo and reverse factor string searching
and reverse factor string searching
and reverse factor string searching algorithms in general work to find a
algorithms in general work to find a
algorithms in general work to find a pattern P within a text team apostille
pattern P within a text team apostille
pattern P within a text team apostille eco Giancarlo uses an efficient
eco Giancarlo uses an efficient
eco Giancarlo uses an efficient algorithm to store characters and find a
algorithm to store characters and find a
algorithm to store characters and find a match instead of comparing every
match instead of comparing every
match instead of comparing every character in pattern P with every
character in pattern P with every
character in pattern P with every character in text T resulting in
character in text T resulting in
character in text T resulting in time-consuming operations apostille eco
time-consuming operations apostille eco
time-consuming operations apostille eco Giancarlo can skip mismatched alignments
Giancarlo can skip mismatched alignments
Giancarlo can skip mismatched alignments when a match is not found reverse factor
when a match is not found reverse factor
when a match is not found reverse factor is faster because it uses a similar
is faster because it uses a similar
is faster because it uses a similar method to apostille eco Giancarlo
method to apostille eco Giancarlo
method to apostille eco Giancarlo but stores data more efficiently so a
but stores data more efficiently so a
but stores data more efficiently so a type of array that allows the computer
type of array that allows the computer
type of array that allows the computer to quickly access data values from
to quickly access data values from
to quickly access data values from memory however I knew that the existing
memory however I knew that the existing
memory however I knew that the existing algorithms couldn’t find a mutation fast
algorithms couldn’t find a mutation fast
algorithms couldn’t find a mutation fast enough so I innovated parallel versions
enough so I innovated parallel versions
enough so I innovated parallel versions of them a parallel program can run on
of them a parallel program can run on
of them a parallel program can run on multiple computer processors how does
multiple computer processors how does
multiple computer processors how does parallelism make an algorithm faster a
parallelism make an algorithm faster a
parallelism make an algorithm faster a parallel program can run on multiple
parallel program can run on multiple
parallel program can run on multiple computer processors parallel computing
computer processors parallel computing
computer processors parallel computing is like weaving a rug where the rug is
is like weaving a rug where the rug is
is like weaving a rug where the rug is the task or searching the genome and the
the task or searching the genome and the
the task or searching the genome and the workers are the processors if one worker
workers are the processors if one worker
workers are the processors if one worker does the job it could take that worker a
does the job it could take that worker a
does the job it could take that worker a month but if ten workers do the job the
month but if ten workers do the job the
month but if ten workers do the job the rug would only take a few days to finish
rug would only take a few days to finish
rug would only take a few days to finish in a nutshell parallelism makes
in a nutshell parallelism makes
in a nutshell parallelism makes processors work together reducing the
processors work together reducing the
processors work together reducing the runtime I found that paralyzing the
runtime I found that paralyzing the
runtime I found that paralyzing the algorithms with four processors
algorithms with four processors
algorithms with four processors accelerated each sequential one by 400%
accelerated each sequential one by 400%
accelerated each sequential one by 400% parallel reverse factor was double the
parallel reverse factor was double the
parallel reverse factor was double the speed of parallel apostille eco
speed of parallel apostille eco
speed of parallel apostille eco Giancarlo accelerating the original
Giancarlo accelerating the original
Giancarlo accelerating the original algorithm by 8
algorithm by 8
algorithm by 8 percent though I obtained my results in
percent though I obtained my results in
percent though I obtained my results in milliseconds with an 800,000 based
milliseconds with an 800,000 based
milliseconds with an 800,000 based excerpt from chromosome 1 each algorithm
excerpt from chromosome 1 each algorithm
excerpt from chromosome 1 each algorithm would take hours to run using a whole
would take hours to run using a whole
would take hours to run using a whole genome the human genome is 6 billion
genome the human genome is 6 billion
genome the human genome is 6 billion bases long making an algorithm take
7,500 times longer to run than in my
7,500 times longer to run than in my experiments
experiments
experiments doctors would run an algorithm to search
doctors would run an algorithm to search
doctors would run an algorithm to search for multiple mutations in the whole
for multiple mutations in the whole
for multiple mutations in the whole genome which could take several hours
genome which could take several hours
genome which could take several hours this might be too late for patients in
this might be too late for patients in
this might be too late for patients in emergency situations therefore an
emergency situations therefore an
emergency situations therefore an accelerated algorithm like mine will
accelerated algorithm like mine will
accelerated algorithm like mine will help save lives by allowing doctors to
help save lives by allowing doctors to
help save lives by allowing doctors to quickly predict a patient’s response to
quickly predict a patient’s response to
quickly predict a patient’s response to a drug I became a finalist in the
a drug I became a finalist in the
a drug I became a finalist in the discovery 3m young scientist challenge
discovery 3m young scientist challenge
discovery 3m young scientist challenge for this research I was able to develop
for this research I was able to develop
for this research I was able to develop my innovation through a summer
my innovation through a summer
my innovation through a summer mentorship with 3m scientist John
mentorship with 3m scientist John
mentorship with 3m scientist John Henderson industry leader and medication
Henderson industry leader and medication
Henderson industry leader and medication risk management software company tabula
risk management software company tabula
risk management software company tabula rasa healthcare invited me to present at
rasa healthcare invited me to present at
rasa healthcare invited me to present at their headquarters though my algorithm
their headquarters though my algorithm
their headquarters though my algorithm was effective I thought I could improve
was effective I thought I could improve
was effective I thought I could improve the feasibility since the procedure for
the feasibility since the procedure for
the feasibility since the procedure for hospitals to access genomes is still not
hospitals to access genomes is still not
hospitals to access genomes is still not firmly established now the good news is
firmly established now the good news is
firmly established now the good news is genome sequencing is becoming more
genome sequencing is becoming more
genome sequencing is becoming more widespread and was expensive to obtain
widespread and was expensive to obtain
widespread and was expensive to obtain some hospitals are already sequencing
some hospitals are already sequencing
some hospitals are already sequencing every newborns genomes demonstrating the
every newborns genomes demonstrating the
every newborns genomes demonstrating the increasing feasibility of personalized
increasing feasibility of personalized
increasing feasibility of personalized medicine however accessing genomes and
medicine however accessing genomes and
medicine however accessing genomes and translating complex medical tests into a
translating complex medical tests into a
translating complex medical tests into a single prescription remains a challenge
I created a prototype of a graphical
I created a prototype of a graphical user interface that shows how I envision
user interface that shows how I envision
user interface that shows how I envision using my innovation to help doctors
using my innovation to help doctors
using my innovation to help doctors access genomes
access genomes
access genomes it shows how I envision using my
it shows how I envision using my
it shows how I envision using my parallel reverse factor algorithm and
parallel reverse factor algorithm and
parallel reverse factor algorithm and medication risk management software to
medication risk management software to
medication risk management software to help save lives now I’m going to show
help save lives now I’m going to show
help save lives now I’m going to show you my prototype my proposed prototype
you my prototype my proposed prototype
you my prototype my proposed prototype would allow doctors to access genomes
would allow doctors to access genomes
would allow doctors to access genomes find mutations and then link them to
find mutations and then link them to
find mutations and then link them to drug responses imagine a patient
drug responses imagine a patient
drug responses imagine a patient collapses with a seizure the patient
collapses with a seizure the patient
collapses with a seizure the patient needs a drug right away the doctor
needs a drug right away the doctor
needs a drug right away the doctor enters the patient’s diagnosis candidate
enters the patient’s diagnosis candidate
enters the patient’s diagnosis candidate drugs and sequence genome the doctor
drugs and sequence genome the doctor
drugs and sequence genome the doctor then runs my parallel reverse factor
then runs my parallel reverse factor
then runs my parallel reverse factor algorithm to gain information about a
algorithm to gain information about a
algorithm to gain information about a potential prescription in the time of
potential prescription in the time of
potential prescription in the time of this eight-minute talk twenty four
this eight-minute talk twenty four
this eight-minute talk twenty four people were hospitalized with an adverse
people were hospitalized with an adverse
people were hospitalized with an adverse drug reaction my innovation has the
drug reaction my innovation has the
drug reaction my innovation has the potential to reduce these
potential to reduce these
potential to reduce these hospitalizations and improve health by
hospitalizations and improve health by
hospitalizations and improve health by advancing the field of personalized
advancing the field of personalized
advancing the field of personalized medicine I envision a future where
medicine I envision a future where
medicine I envision a future where treatment is tailored to one’s
treatment is tailored to one’s
treatment is tailored to one’s individual genome where someone can take
individual genome where someone can take
individual genome where someone can take a drug and not just hope for but expect
a drug and not just hope for but expect
a drug and not just hope for but expect a positive outcome bioinformatics
a positive outcome bioinformatics
a positive outcome bioinformatics research similar to mine can soon
research similar to mine can soon
research similar to mine can soon transform this vision into reality by
transform this vision into reality by
transform this vision into reality by allowing doctors to make the pill fit
allowing doctors to make the pill fit
allowing doctors to make the pill fit the ill thank you
the ill thank you
the ill thank you [Applause]
”
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