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SUMMARY:(Un-)supervised Learning of Cell Population Structure from Single-
 Cell Snapshot Data
DTSTART:20190204T121500
DTSTAMP:20260503T095407Z
UID:288a6665258fa1ef2a642b8448d625fcb1918d70d0a77841387a54b2
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Manfred Claassen\, ETH Zürich (CH)\nJOINT CHEMICAL and 
 BIOENGINEERING SEMINAR\n(sandwiches served)\n\nAbstract:\nRare cell popula
 tions play a pivotal role in the initiation and progression of diseases su
 ch as cancer. However\, the identification of such subpopulations remains 
 a difficult task. I will present our representation learning approaches to
  detect rare cell subsets associated with disease using high-dimensional s
 ingle-cell measurements and demonstrate identification of rare CMV infecti
 on and multiple sclerosis-associated cell subsets in peripheral blood\, an
 d extremely rare leukemic blast populations in minimal residual disease-li
 ke situations with frequencies as low as 0.01%\, as well as identification
  of morphological patterns associated with cancer severity.\n\nBio:\nManfr
 ed Claassen joined the Institute of Molecular Systems Biology at the ETH Z
 urich as an Assistant Professor for computational biology in January 2013.
 \nHe has carried out parallel studies in Biochemistry and Computer Science
  at the University of Tübingen and been awarded a Diploma in Biochemistry
  in 2004 and a Diploma in Computer Science in 2006. In 2010 he obtained a 
 PhD from ETH Zurich. During his doctoral studies he developed statistical 
 methods to design and validate proteome measurements. In 2011 he moved on 
 for postdoctoral training with Daphne Koller at Stanford University\, wher
 e he focused on inferring informative network models from single cell reso
 lved perturbation studies.\nHis research aims at elucidating the compositi
 on of heterogeneous cell populations and how these implement function in t
 he context of cancer and immune biology by jointly evaluating single cell 
 and genome wide measurements. The Claassen group builds on concepts from s
 tatistics\, machine learning and mathematical optimization to develop prob
 abilistic approaches to describe biological systems\, learn these descript
 ions from data and to design experiments to validate hypotheses following 
 from computational analyses.
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717
STATUS:CONFIRMED
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