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SUMMARY:Compressed Sensing in Cancer Biology?  (A Work in Progress) - Prof
 . M. Vidyasagar FRS\, Univ. of Texas\, Dallas
DTSTART:20121123T140000Z
DTEND:20121123T153000Z
UID:TALK41634@talks.cam.ac.uk
CONTACT:Dr Jason Z JIANG
DESCRIPTION:Unlike traditional talks in which the speaker presents a reaso
 nably finished body of work\, in this talk I will touch only briefly upon 
 recently completed research\, and then suggest several problems that deser
 ve the attention of quantitatively minded researchers.  The title refers t
 o the fact that an algorithm recently developed by us for predicting the r
 esponse of cancer patients to specific therapies has a lot of similarities
  with recent research in compressed sensing based on l_1-norm minimization
 .  However\, the proof techniques used to analyze the behavior of compress
 ed sensing algorithms do not apply to our algorithm.  Nevertheless our alg
 orithm has performed remarkably well on a range of data sets.  So the ques
 tion is: Why?  Another theme is that contemporary biology data sets consis
 t of a mixture of numerical data and class labels.  So new methods are nee
 ded to "integrate" all of these diverse types of data and draw meaningful 
 conclusions.  Some possible approaches to data integration will also be su
 ggested.
LOCATION: Cambridge University Engineering Department\, Lecture Room 12
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