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BEGIN:VEVENT
DTSTAMP:20260426T131954Z
DTSTART;VALUE=DATE-TIME:20200115T150000
DTEND;VALUE=DATE-TIME:20200115T160000
SUMMARY:CRiSM Seminar - Deep learning in genomics\, and a topic model for
  single cell analysis - Gerton Lunter
TZID:Europe/London
UID:20200115-8a17841a6e6469f6016e65656fe10b41@warwick.ac.uk
CREATED:20200109T133223Z
DESCRIPTION:In the last decades biology has become an increasingly data-r
 ich science. In parallel\, the field of Machine Learning (ML) has made r
 emarkable progress in modeling large and complex data sets. This suggest
 s to try and apply ML techniques to problems in biology. In the first ha
 lf of this talk I will show how ML methods can predict various intermedi
 ate phenotypes from sequence\, including splicing and chromatin state. T
 he nature of DNA means that these models often exhibit reverse-complemen
 t symmetry\, and we found that explicitly dealing with this structure im
 proves the quality of the model. For training we use data from whole-gen
 ome epigenetic assays across a range of tissue types. To address tissue 
 heterogeneity the field is moving towards single-cell assays\, and to id
 entify the specific cell types document topic models such as Latent Diri
 chlet Allocation have been used. In the second half of the talk I will d
 iscuss recent work where we extend LDA to allows topic usage to be posit
 ive correlated across cell types\, which we hope will improve model fit 
 and increase the sensitivity of detecting rare cell types.
LOCATION:MB0.07
CATEGORIES:CRiSM Seminars,Seminars
LAST-MODIFIED:20200109T133223Z
ORGANIZER;CN=Paula Matthews:
END:VEVENT
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