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DESCRIPTION:Speaker: Katarzyna Bozek\nhosted by Gasper Tkacik\nAbstract: Be
 fore large datasets became pervasive in all domains\, life sciences and bi
 ology among them were generating copious and complex data. In my talk I wi
 ll present through a range of example problems\, the evolution and growtho
 f data analysis in this field. HIV-host interaction\, human evolution\, or
  novel ways of quantifying natural behaviorthe broad spectrum of applicati
 ons that I will describe is tied by a common challenge of finding patterns
  and meaning in large and complex data.Currently\, new machine learning me
 thods to extract information from images have opened vast new opportunitie
 s to data analysis in biology. Traditionally\, categories or states of obj
 ects in bioimages are determined based on humandefined relevant elements a
 nd the recent advances in deep learning have allowed to largely automate t
 his task by providing adaptable and precise methods for image segmentation
 \, object recognition\, and classification. However\,what if the human eye
  is not capable of perceiving potentially meaningful visual features\, or 
 when the observed objects are inherently complex? In my talk I will demons
 trate through my most recent work on marker-less honeybee tracking and qua
 ntifying behavior of C. elegans how the new machine learning methods for i
 mage analysis are capable of quantifying patterns defying human vision. Th
 e methods I will present allow to distinguish apparently identical bees\, 
 quantify worm posture\, predict temporal patterns in movement and behavior
 . The new ways ofextracting signals from visual data\, offer an unpreceden
 ted opportunity to transform image data into another typeof large and comp
 lex data type that is intractable to human perception.Computational analys
 is can bring insights into biological questions\, while challenging datase
 ts in biology can giverise to innovative computational solutions. Biology 
 is a source of rich and complex datasets which serve not onlyanswering imp
 ortant scientific questions but also offer opportunities for creation of i
 nnovative solutions for data analysis. Similar to how large sequence datas
 ets gave rise to efficient sequence alignment algorithms that in turn allo
 wed for quantitative sequence analysis at an unprecedented scale\, current
 ly\, biological images and new machine learning methods for their analysis
  offer an opportunity for another quantitative leap in biological data ana
 lysis.
LOCATION:Mondi Seminar Room 2\, Central Building\, ISTA
ORGANIZER:tguggenb@ist.ac.at
SUMMARY:Katarzyna Bozek: Data science of biology
URL:https://talks-calendar.ista.ac.at/events/1725
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