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DTSTAMP:20260617T142924Z
UID:e447defa-fcae-451b-a7a1-21e559ee2bf3
DTSTART:20210802T090000Z
DTEND:20210806T170000Z
DESCRIPTION:Educators:\nAltuna Alkalin\, Verdan Franke\, Bora Uyar\, Jan Do
 hmen\, Artem Baranovsky (RBC/deNBI-epi Scientists from Berlin)\n\nDate:\nA
 ugust - September 2021\n\nLocation:\nOnline\n\nContents:\n\nThe general ai
 m of the course is to equip participants with practical and technical know
 ledge to deploy machine learning methods on genomic data sets. With this a
 im in mind\, we will go through certain statistical concepts and move on t
 o unsupervised and supervised machine learning methods to analyze high-dim
 ensional data sets.\nThis will be an online training event which will be m
 ostly asynchronous. A typical module would comprise of lectures followed b
 y hands-on exercises and a quiz. The participants will have a week to comp
 lete the lectures and exercises for each module at their own pace and at t
 he time of their choosing within that week. Only the participants who comp
 lete the exercises and a quiz in a timely manner and have at least 50% of 
 the tasks in the exercises will be invited to the capstone project. The ca
 pstone project tasks are designed using data from a real world problem. Th
 e participants who provide the best reports for the capstone projects will
  be invited to co-author a manuscript with the Akalin lab.\nMost tasks can
  be done on a regular laptop. A recent version of R and Rmarkdown is neces
 sary to complete the hands-on exercises.\n\n    Module 1: Statistics for g
 enomics\n    Module 2: Unsupervised learning and applications in genomics\
 n    Module 3: Supervised learning and applications in genomics\n    Modul
 e 4: Capstone project: Drug response prediction using genomic data\n\nLear
 ning goals:\nThe course will be beneficial for first year computational bi
 ology PhD students\, and experimental biologists and medical scientists wh
 o want to begin data analysis or are seeking a better understanding of com
 putational genomics and analysis of popular sequencing methods.r\n\nPrereq
 uisites:\nSome statistics and R programming experience will be good to kee
 p up with the course. Practicals will be done in R.\n\nKeywords:\nComputat
 ional genomics\, RNA-seq\, Machine learing\,\n\nTools:\nR/Bioconductor\n\n
 Application Deadline: 30th of June
SUMMARY:Computational genomics course for hands-on data analysis 2021 - Mac
 hine Learning for Genomics
URL;VALUE=URI:https://www.denbi.de/training/1227-computational-genomics-cou
 rse-for-hands-on-data-analysis-2020
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