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SUMMARY:Using machine learning to make maps of stem cell differentiation -
  Dr. Jake Yeung\, Hubrecht Institute for Developmental Biology and Stem Ce
 ll Research
DTSTART:20200106T163000Z
DTEND:20200106T173000Z
UID:TALK136567@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:Our bodies have a remarkable capacity to repair and self-renew
  through tissue stem cells. How tissue stem cells make cellular decisions 
 by coordinating the abundance and activity of thousands of RNAs and protei
 ns to regulate tissue function is not well understood.\n\nGene expression 
 can be controlled by how the DNA is packaged inside the cell (called chrom
 atin). For example\, compacting DNA can turn specific genes off while deco
 mpacting and turn specific genes on. During stem cell differentiation\, th
 e chromatin changes to support the new expression state. We recently devel
 oped a high-throughput method to profile this DNA packaging in single cell
 s.\n\nIn this talk\, I will focus on the goals and challenges of analyzing
  this data. In particular\, I will present machine learning strategies tha
 t model the sparse data and project onto lower-dimensional manifolds in or
 der to characterize the chromatin state along stem cell differentiation. I
  will also discuss systems biology methods that enable physically meaningf
 ul interpretations of the data in terms of protein regulators that underli
 e changes in chromatin state. \n\nOverall\, combining machine learning met
 hods with systems biology creates maps of cellular differentiation and pro
 vides interpretable tools to understand and probe these maps. 
LOCATION:Mott Seminar (531) room\, top floor of the Mott Building\, in the
  Cavendish Laboratory\, West Cambridge.
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