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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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B1 cancer computational unnecessary therapy trial biology

Computational biology of cancer

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    alex posted on 2016/03/23
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