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disease. And in a recent series of trials, first published in 2017 in
the medical journal The Lancet, GNS has detailed REFS’s poten-
tial when applied to a disease such as Parkinson’s—an ailment
in which pleiotropic factors render existing treatments wildly
hit-or-miss in their effectiveness.
With Parkinson’s, the network of interactions set in motion
by defective genes has a particular shape to it, and the break-
down of motor functioning is the most reliable indication of its
progression. Feeding the genetic data of Parkinson’s sufferers
and a control group into REFS helped GNS generate over 100
computer models depicting what might be going on as mo-
tor function deteriorates. The models can uncover previously
unknown genetic mutations that may contribute to the speedup
of deterioration.
But that’s just the first part. GNS has used those findings to
create 5,000 different computer simulations of randomized
control trials, each aiming to predict how fast the disease would
progress with varying approaches to treatment. Such speed-
testing can be vastly more economical than seeking the same
result through controlled human trials. And GNS, in partner-
ship with other drugmakers, is now applying similar approaches
to treatments for diabetes, ALS, multiple myeloma, and breast
cancer, among other diseases.
“We now have the ability to create and construct, on the
computer, representations of human patients and their diseases
such that we can now probe, drug by drug, care management
intervention by care management intervention, and say what
treatments work for which patient,” says Colin Hill, CEO of GNS.
The simulation, in other words, is not just finding correlations:
It is answering What if questions. What if we had given drug A in-
stead of drug B to patient X? That ability to simulate and answer
counterfactuals is a recent arrival in the practice of A.I. It owes
its growing importance in large part to GNS’s technology adviser,
Judea Pearl, a longtime A.I. researcher and professor of computer
science at UCLA. In a popular volume published last year called
The Book of Why, Pearl describes how true intelligence ascends
from merely noticing patterns, which machine learning does in
spades, to being able to express counterfactual reasoning about
what would have happened, based on those patterns. Data alone,
disconnected from any idea of a mechanism, doesn’t provide real
insight. “Data is profoundly dumb about causality,” claims Pearl.
Hill puts it more bluntly: “Deep learning is not that deep.”D
ANIEL COHEN, NOW 67, spent his childhood in Tunisia’s
heterogeneous society of Jews, Christians, Muslims,
“living all together in a very elegant and pacific way.”
He credits that experience for his taste for “things
that are not complicated, but complex.” When he was 9, Co-
hen’s family immigrated to Paris, where he pursued the piano
avidly. He switched to medicine once he realized he might have
a greater impact as a scientist than a musician, but the passion
has not left him. He has been a guest conductor at the Royal
Philharmonic in London and dreams of leading that ensemble
in Tchaikovsky’s Symphony Pathétique. “The predisposition to
orchestra conductor, CEO, and scientist are all controlled by the
same genes,” he jokes.
Parlaying genomics and technology into pharmaceutical suc-approval of new therapies; in spiraling costs for
drug development (what a Tufts study recently
identified as “the $2.6 billion pill”); and in the
soaring prices of the few treatments that break
new ground, such as the $475,000 cost of a
course of treatment with Novartis’s leukemia
drug Kymriah.
More recently, researchers have begun to
grapple with biological complexity with the
help of the science of networks. That science’s
chief evangelist is Albert-László Barabási, a
professor at Northeastern University whose
2014 book Linked popularized the notion that
network theory can explain numerous fields,
from fashion trends to sexual relations to dis-
ease. Barabási and others realized that disease
is like a bad signal that moves through a net-
work of connections from genes to proteins to
cells to tissues, until all these “perturbations”
manifest as the familiar symptoms of disease.
Complicated diseases are confluences of
numerous effects, because pleiotropy means
that any given protein can act at different
points in the body. Startups like Pharnext as-
sume that drugs can also be pleiotropic, acting
on more than one protein and more than one
interaction in the body at the same time. To
find a drug combination capable of tackling
complexity, the enormous power of machine
learning, with its ability to spot patterns in
data, must be wedded to a sense of the struc-
ture by which disease operates.
This, in turn, has required an evolution in
the relationship between computer scientists
and biologists. Newer machine- learning ap-
proaches ingest vastly more data and can as-
semble hierarchies of information that let them
go beyond correlation. Still, harnessing these
“deep learning” neural networks into a struc-
ture that has any predictive power requires
some elegant algorithm-building.
Colin Hill, CEO and founder of GNS
Healthcare, is one of the builders. His com-
pany, based in Cambridge, Mass., has spent
18 years developing a computer system called
REFS, which stands for “reverse engineering,
forward simulation.” GNS has raised a total of
$38 million over the years—from Amgen Ven-
tures, the venture capital arm of the drug gi-
ant, along with Celgene and a variety of other
investors—to build and fine-tune its models of