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2 events found

Scientific topics: Data mining 

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Country: United Kingdom 

  • Analysis of bulk RNA-seq data (ONLINE LIVE TRAINING)

    18 - 20 November 2020

    Cambridge, United Kingdom

    Elixir node event
    Analysis of bulk RNA-seq data (ONLINE LIVE TRAINING) https://tess.elixir-europe.org/events/analysis-of-bulk-rna-seq-data-online-live-training-018bc625-9d97-4f56-97fa-4a78c1f5e75e PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout. The aim of this course is to familiarize the participants with the primary analysis of RNA-seq data. This course starts with a brief introduction to RNA-seq and discusses quality control issues. Next, we will present the alignment step, quantification of expression and differential expression analysis. For downstream analysis we will focus on tools available through the Bioconductor project for manipulating and analysing bulk RNA-seq. Please note that if you are not eligible for a University of Cambridge [Raven](http://www.ucs.cam.ac.uk/docs/faq/raven/n5) account you will need to book or register your interest by linking [here](http://bioinfotraining.bio.cam.ac.uk/booking-form/?event-id=3593329&course-title=Analysis%20of%20bulk%20RNA-seq%20data).'' 2020-11-18 09:30:00 UTC 2020-11-20 17:30:00 UTC University of Cambridge Craik-Marshall Building, Cambridge, United Kingdom Craik-Marshall Building Cambridge United Kingdom CB2 3AR RNA-Seq Data mining Transcriptomics Data visualisation Functional genomics Bioinformatics University of Cambridge Bioinformatics Training [] Graduate studentsPostdocs and Staff members from the University of CambridgeInstitutions and other external Institutions or individuals workshops_and_courses [] HDRUK
  • An Introduction to Machine Learning (ONLINE LIVE TRAINING)

    23 - 25 November 2020

    Cambridge, United Kingdom

    Elixir node event
    An Introduction to Machine Learning (ONLINE LIVE TRAINING) https://tess.elixir-europe.org/events/an-introduction-to-machine-learning-online-live-training-ca34af0c-9423-4e3c-bfe3-539c1ed8b05d PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout. Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences. Lectures will introduce commonly used algorithms and provide insight into their theoretical underpinnings. In the practicals students will apply these algorithms to real biological data-sets using the R language and environment. Please be aware that the course syllabus is currently being updated following feedback from the last event; therefore the agenda below will be subjected to changes. Please note that if you are not eligible for a University of Cambridge [Raven](http://www.ucs.cam.ac.uk/docs/faq/raven/n5) account you will need to book or register your interest by linking [here](http://bioinfotraining.bio.cam.ac.uk/booking-form/?event-id=3590664&course-title=An%20Introduction%20to%20Machine%20Learning).'' 2020-11-23 09:30:00 UTC 2020-11-25 17:00:00 UTC University of Cambridge Craik-Marshall Building, Cambridge, United Kingdom Craik-Marshall Building Cambridge United Kingdom CB2 3AR Machine learning Data mining University of Cambridge Bioinformatics Training [] This is aimed at life scientists with little or no experience in machine learning and that are looking at implementing these approaches in their research.Graduate studentsPostdocs and Staff members from the University of CambridgeInstitutions and other external Institutions or individuals workshops_and_courses [] HDRUK

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