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3 ways artificial intelligence is tackling cancer

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This article or excerpt is included in the GLP’s daily curated selection of ideologically diverse news, opinion and analysis of biotechnology innovation.

Case Study 1: The Sharing Project

[Jinghui Zhang wondered] how much faster cancer research could move if data sets were freely and immediately available to computational biologists who wanted to work on them, so in April she partnered with Microsoft to release St. Jude Cloud, a web-based data processor that offers access—within 48 hours—to the largest publicly available set of childhood cancer stats in the world, the whole genomes of more than 5,000 St. Jude patients.

Case Study 2: Machine Learning for Doctors

In January 2018, Microsoft also partnered with the charity Stand Up to Cancer on an $11 million research program called Convergence 2.0 to solve a problem that is essentially the reverse of the one Zhang faced: researchers who have data sets or ideas worth studying, but little or no computer programming expertise.

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Convergence 2.0 funded seven teams of researchers and medical doctors, matching each with machine-learning experts from places like Microsoft and MIT.

Case Study 3: Sharable Tumor Samples

Meanwhile, for clinicians, a new company called Paige.AI made an agreement with Memorial Sloan Kettering Cancer Center in New York to train machine-learning algorithms on its absolutely massive collection of 25 million digitized tumor slides. The software will eventually be able to help doctors all over the country diagnose cancer from biopsies the same way a top MSK pathologist would.

Read full, original post: Cancer Has a New Enemy: A.I.

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