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X-WR-CALNAME:Effective representation learning to dissect the gene regulato
ry grammar
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UID:2021-05-24-jing-zhang@cml.ics.uci.edu
DTSTAMP:20210524T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20210524T130000
DTEND;TZID=America/Los_Angeles:20210524T140000
SUMMARY:[CML Seminar] Jing Zhang: Effective representation learning to diss
ect the gene regulatory grammar
LOCATION:Online (live stream)
DESCRIPTION:Jing Zhang\, Assistant Professor\, Department of Computer Scien
ce\, University of California\, Irvine\n\nTitle: Effective representation
learning to dissect the gene regulatory grammar\n\nAbstract: Recent advanc
es in sequencing technologies provide unprecedented opportunities to decip
her multi-scale gene regulatory grammars at diverse cellular states. I int
roduce our computational efforts on cell/gene representation learning to e
xtract biologically meaningful information from high-dimensional\, sparse\
, and noisy genomic data. First\, we proposed SAILER\, a deep generative m
odel to learn low-dimensional latent cell representations from single-cell
epigenetic data that are invariant to confounding factors. Then at the ne
twork level\, we developed TopicNet using latent Dirichlet allocation to e
xtract latent gene communities and quantify regulatory network rewiring be
tween cell states\, applied across 13 cancer types.\n\nhttps://cml.ics.uci
.edu/seminars/2021-05-24-jing-zhang
X-ALT-DESC;FMTTYPE=text/html:Jing Zhang\, Assistant Prof
essor\, Department of Computer Science\, University of California\, Irvine
Title: Effective representation learning to dissect the gen
e regulatory grammar
Abstract: Recent advances in sequencing
technologies provide unprecedented opportunities to decipher multi-scale
gene regulatory grammars at diverse cellular states. I introduce our compu
tational efforts on cell/gene representation learning to extract biologica
lly meaningful information from high-dimensional\, sparse\, and noisy geno
mic data. First\, we proposed SAILER\, a deep generative model to learn lo
w-dimensional latent cell representations from single-cell epigenetic data
that are invariant to confounding factors. Then at the network level\, we
developed TopicNet using latent Dirichlet allocation to extract latent ge
ne communities and quantify regulatory network rewiring between cell state
s\, applied across 13 cancer types.
https://cml.ics.uci.edu/seminars/2021-05
-24-jing-zhang
URL:https://cml.ics.uci.edu/seminars/2021-05-24-jing-zhang
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