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SUMMARY:Gone fishing. Machine-learning guided discovery from public data. 
 - Professor Casey Greene from the Perelman School of Medicine at Universit
 y of Pennsylvania 
DTSTART:20180921T120000Z
DTEND:20180921T130000Z
UID:TALK109969@talks.cam.ac.uk
CONTACT:72001
DESCRIPTION:Our understanding of the world\, and maladies such as cancer\,
  improves when scientists metaphorically go fishing. However\, the term "f
 ishing expedition" is primarily used as a pejorative when applied to scien
 tific projects.\nProfessor Greene will provide his perspective on why scie
 nce could become more accurate and efficient once we rescue this term. Sci
 entific 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 individ
 ual 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 Profess
 or Greene  will share one or two cancer-related stories from their own wor
 k. Finally\, he will discuss why he thinks large collections of public dat
 a are a uniquely valuable resource for the process of scientific discovery
 .\n
LOCATION:CRUK CI Lecture Theatre (Room 001)
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