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X-WR-CALNAME:Institute for Quantitative and Computational Biosciences
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X-WR-CALDESC:Events for Institute for Quantitative and Computational Biosciences
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DTSTART;TZID=America/Los_Angeles:20181018T093000
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DTSTAMP:20180910T174834Z
CREATED:20180823T210747Z
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UID:7303-1539855000-1539862200@qcb.ucla.edu
SUMMARY:RNA-Seq Analysis I - Day Three
DESCRIPTION:RNA sequencing is used to examine the continuously changing cellular transcriptome. First\, this workshop provides an introduction and the basics tools to process raw RNA-seq data on a cluster machine (Hoffman2). This workshop can serve as a starting point to develop a gene expression project. To facilitate learning\, we will explore a hands-on tutorial that guides students in processing the data from raw reads through read counts. We will use a real-case study-based approach appropriate for Illumina read data.
URL:https://qcb.ucla.edu/event/rna-seq-analysis-i-day-three/
LOCATION:The Collaboratory\, 610 Charles E Young Drive East - BOYER HALL ROOM 529\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:Interactive Workshop
ATTACH;FMTTYPE=image/png:https://qcb.ucla.edu/wp-content/uploads/sites/14/2018/08/2018_10_16-RNAseq1.png
ORGANIZER;CN="QCB Collaboratory":MAILTO:collaboratory@ucla.edu
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DTSTART;TZID=America/Los_Angeles:20181018T130000
DTEND;TZID=America/Los_Angeles:20181018T160000
DTSTAMP:20180910T173906Z
CREATED:20180823T210749Z
LAST-MODIFIED:20180910T173906Z
UID:7306-1539867600-1539878400@qcb.ucla.edu
SUMMARY:Machine Learning in Python - Day Three
DESCRIPTION:Applications of Machine Learning can be used to analyze biological data without the need of advanced programming skills. For example\, Machine Learning techniques can be used to construct predictive models based on a set of training examples\, to remove noise and spurious artifacts from data (e.g. photobleaching)\, or to help visualize trends within high dimensional datasets\, etc. First\, this workshop introduces the basic principles involved in the applications mentioned above\, such as pattern recognition\, linear and non-linear regression\, and cluster analysis. This workshop will be oriented towards hands-on activities\, starting from the basics of how to load and prepare biological datasets in a Python environment. By the end of this workshop\, students will be able to use Scikit-Learn’s documentation (and other libraries) to build models based on their own data\, assess their performance\, and make new predictions. Although no advanced knowledge will be assumed\, students are encouraged to attend to the Advanced Python and Modern Statistics workshops.
URL:https://qcb.ucla.edu/event/machine-learning-in-python-day-three/
LOCATION:The Collaboratory\, 610 Charles E Young Drive East - BOYER HALL ROOM 529\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:Interactive Workshop
ATTACH;FMTTYPE=image/png:https://qcb.ucla.edu/wp-content/uploads/sites/14/2018/08/2018_10_16-Machine-Learning-with-Python.png
ORGANIZER;CN="QCB Collaboratory":MAILTO:collaboratory@ucla.edu
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