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MATHUKUMALLI VIDYASAGAR: We work side by side with the
biologists and help them to interpret and get knowledge of
all the experimental data that they're generating. That's
what computational biology is about. My current research is
called computational cancer biology. What we are talking
about is once you get cancer, what should be the therapy?
I spent about a year or two talking to cancer doctors and
say what is it that you see as the biggest challenges? So it
took me a while to figure out what is the intersection, the
question that bothered them and the question that I can answer
and finally we zeroed in on this personalized cancer therapy for
specific forms of cancer of the uterus. Can you predict which
patients will respond well and which patients do not respond
well to specific therapy? So they had certain guidelines that
when the patient's tumor was more than two centimeters in
diameter, in addition to taking out the uterus and all the
associated parts; they also removed the so called lymph
nodes for fear that the cancer had already spread there. And
then when they did the analysis after the surgery, over a very
long period they discovered that 78% of the surgeries were
unnecessary. So they had perfect knowledge that most of the
surgeries were unnecessary but no way of predicting beforehand
which were unnecessary. So this was the challenge they posed to
us. Can you find some indicators? So this took about
six to eight months of algorithm development, new computational
methods and then we had reasonably well working
predictive procedure. Then we collected about 28 new tumors.
We applied our predictive methodology on those and out of
these 28 people, only nine of them really required surgery of
the lymph node, and we were able to spot eight out of
those nine. So they're really happy with that.
There's still a lot of people out there who think
that the way to solve problems of cancer is trial
and error. So to change the mindset and say you don't
have to rely on trial and error, you don't have to rely on
serendipity, you can actually undertake systematic analysis of
large amounts of data to come out with plausible hypothesis,
only a few people accept this now. So I'm hoping that through
our work we'll come to a situation where this approach
essentially becomes natural. People should say why would you
want to do trial and error? This is the way to go.
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