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SUMMARY: Computational analyses of high-throughput spatial proteomics data
  - Dr. Laurent Gatto\, Department of Biochemistry
DTSTART:20130306T140000Z
DTEND:20130306T150000Z
UID:TALK43871@talks.cam.ac.uk
CONTACT:Danielle Stretch
DESCRIPTION:In biology\, localisation is function. Knowledge of the locali
 sation of proteins within the cell is of paramount importance to assess an
 d study their function. Generation of high quality data is an essential an
 d challenging task\, and multiple research groups have described various e
 fforts and technical procedures to obtain such data. However\, data analys
 is is as critical as data production for insightful\nbiological interpreta
 tion\, and no consistent and robust solutions have been offered to the com
 munity so far. I will first present classical state-of-the-art data analys
 is methodologies\, centred around supervised machine learning approaches\,
  describe their inherent problems and recent breakthroughs that permit new
  insights into the data. The second part will compare sequence- and experi
 ment-based approaches\, and illustrate how these complementary data source
 s can be utilised to improve mining of the data. Finally\, I will highligh
 t some avenues on how computational understanding of the data can be used 
 to optimise the experimental design and thus improve our understanding of 
 the underlying biology.\n\nThe talk is part of the CCBI seminar series and
  the DTP graduate course Reviews in Computational Biology\, but is open to
  all attendees.\n\n\n
LOCATION:MR4\, Centre for Mathematical Sciences\, Wilberforce Road\, Cambr
 idge
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