University of Cambridge > Talks.cam > Seminars on Quantitative Biology @ CRUK Cambridge Institute  > Gone fishing. Machine-learning guided discovery from public data.

Gone fishing. Machine-learning guided discovery from public data.

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Our understanding of the world, and maladies such as cancer, improves when scientists metaphorically go fishing. However, the term “fishing expedition” is primarily used as a pejorative when applied to scientific projects. Professor Greene will provide his perspective on why science could become more accurate and efficient once we rescue this term. Scientific fishing expeditions are valuable because they can take research in new directions. Looking at other people’s data can provide researchers with a new perspective on their area of study. However, analysing individual experiments from other labs is difficult. Instead of single datasets, Greene’s lab builds “nets” that they and other scientists can use to go fishing in large collections of public data comprised of many experiments. They have been put to the test on a few recent expeditions, and Professor Greene will share one or two cancer-related stories from their own work. Finally, he will discuss why he thinks large collections of public data are a uniquely valuable resource for the process of scientific discovery.

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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