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X-WR-CALNAME:Munich Startup
X-ORIGINAL-URL:https://cms.munich-startup.net
X-WR-CALDESC:Veranstaltungen für Munich Startup
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DTSTART:20160327T010000
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DTSTART:20161030T010000
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DTSTART;TZID=Europe/Berlin:20161214T190000
DTEND;TZID=Europe/Berlin:20161214T220000
DTSTAMP:20260505T112240
CREATED:20161212T085755Z
LAST-MODIFIED:20161212T085755Z
UID:17039-1481742000-1481752800@cms.munich-startup.net
SUMMARY:Munich Datageeks - December Edition
DESCRIPTION:It is time for the next Munich Datageeks Meetup. In December\, ProSieben Sat.1 will host it for the first time. \nFormat:\n\n2 presentations (each ca. 30-40 min incl. discussion)\nOf course time for networking + food + drinks before\, in between and especially after the presentations\nTalks are held in English\n\nThe line up:\nMartin Preusse & Gökcen Eraslan – Deep modeling of DNA sequences \nAbstract: \nOrdered sequences of molecules are the central concept of biological information. The DNA is composed of the 4 nucleotides (A\, C\, G\, T) and is used to persist information over generations. Our understanding of these DNA sequences is very limited. We cannot read them like a book\, we still have not learned their grammar and vocabulary. Consequently\, there is no way to predict the biological purpose of a stretch of DNA from the sequence alone.\nDeep learning methods offer new ways to bring light into the darkness of our genome and to elucidate the structure of genes and their regulation. However\, the interpretability of deep models and difficulties with modeling long\, variable-length sequences hinder the use of deep learning in biology.\nWe are working on new approaches to deal with these issues. Examples are sequence classification using convolutional neural networks and generative models for variable-length sequences using recurrent variational autoencoders. In this talk\, we will give an overview of biological sequences\, their fascinating properties and their relevance for disease biology. We will demonstrate some of our methods and their application. Finally\, we will show some general ideas drawn from our research which are relevant for other topics. \nBio: \nMartin Preusse:  PhD in computational biology. Currently working on data solutions for biomedical research at Helmholtz Zentrum München and the startup KNOWING. Using Python and noSQL databases. \nGökcen Eraslan:  PhD student in computational biology at Helmholtz\nZentrum München with the main research focus of machine learning\napplications to computational biology problems. \nSuresh Pillai – Marketing Analytics – Putting the science into data science \nAbstract: TBA \nBio: TBA \nHier findet Ihr mehr Informationen zu den Munich Datageeks und der Veranstaltung.
URL:https://cms.munich-startup.net/veranstaltung/munich-datageeks-december-edition/
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