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- European Bioinformatics Institute (EBI)7
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Scientific topic
- Biological modelling3
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Event type
- Workshops and courses7
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Target audience
- Applicants should be researchers who are using large multi-omics datasets to infer systems biology models. This is an advanced-level course, and so we will select applicants who already have some experience (ideally 1-2 years) of working with systems biology modelling or related large-scale multi-omics data analysis. Additionally, applicants will be expected to have a working knowledge of using Linux commands, and experience of using a programming language (e.g. Python or Perl).1
- The course is aimed at individuals working in immunology research who have minimal experience in bioinformatics. Applicants are expected to be at an early stage of using bioinformatics in their research with the need to develop their skills and knowledge further. Participants will require a basic knowledge of the Unix command line, and the R statistical package. We recommend these free tutorials: Introduction to the Unix environment – http://www.ee.surrey.ac.uk/Teaching/Unix/ Basic R concepts – http://www.r-tutor.com/r-introduction1
- This course is aimed at experimental biologists, bioinformaticians and mathematicians who have just started in systems biology, are familiar with the basic terminology in this field and who are now keen on gaining a better knowledge of systems biology modelling approaches to understand biological and biomedical problems. A working knowledge of the Linux operating system and ability to use the command line or experience of using a programming language (e.g Python) would be a benefit but is not mandatory. An undergraduate knowledge of molecular and cellular biology or some background in mathematics is highly beneficial. 1
- This course is for researchers and clinicians at any career stage attending ENDO2019 who want to explore and use the EMBL-EBI IntAct and Reactome resources for their research, whether they are looking to build networks prior to omics data analysis, or to better understand the biology of their favourite gene(s) or protein(s). No knowledge of programming is required, but an undergraduate level knowledge of biology and/or biochemistry would be an advantage.1
- This introductory course is aimed at bench-based biologists, who are involved in, or embarking on projects that will use network and pathway analysis or protein interaction data. For example, you may be using these tools in the interpretation of biological datasets or as part of a systems biology approach. The course requires no prior knowledge of pathway analysis or computer programming skills. Preference will be given to those actively involved in or commencing interaction/pathway-based projects.1
- This introductory course is aimed at biologists who are embarking on multiomics projects and computational biologists / bioinformaticians who wish to gain a better knowledge of the biological challenges presented when working with integrated datasets. Some practical sessions in the course require a basic understanding of the Unix command line and the R statistics package. If you are not already familiar with these then please ensure that you complete these free tutorials before you attend the course: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Basic R concept tutorials: www.r-tutor.com/r-introduction For advanced-level training in using large-scale multiomics data and machine learning to infer biological models you may wish to consider our course on Systems Biology: From large datasets to biological insight.1
- This introductory course is aimed at biologists who are embarking on multiomics projects and computational biologists / bioinformaticians who wish to gain a better understanding of the biological challenges when working with integrated datasets. No programming or command line experience is required to attend this course. Please note this course does not cover statistical approaches for data integration. For advanced-level training in using large-scale multiomics data and machine learning to infer biological models you may wish to consider our course on Systems Biology: From large datasets to biological insight.1
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Instructor
- Pablo Porras Millan
- Aleena Mushtaq29
- Benjamin Moore18
- David Armstrong17
- Michal Szpak17
- Nancy George17
- Birgit Meldal15
- Sarah Morgan14
- Tom Hancocks14
- Alexandra Holinski13
- Baron Koylass13
- Emily Perry13
- Anna Swan12
- Dayane Rodrigues Araujo12
- Sam Holt12
- Kalpana Panneerselvam11
- Lee Larcombe11
- Alex Bateman10
- Alexandre Bonvin10
- Krishna Kumar Tiwari10
- Lorna Richardson10
- Rossana Zaru10
- Wendi Bacon10
- Louisse Paola Mirabueno9
- Osman Salih9
- Rahuman Sheriff9
- Silvie Fexova9
- Thawfeek Mohamed Varusai9
- Typhaine Paysan-Lafosse9
- Ajay Mishra8
- Alexey Larionov8
- Andrew Hercules8
- Cath Brooksbank8
- Eliot Ragueneau8
- Fabio Madeira8
- Hema Bye-A-Jee8
- Emily Bowler-Barnett7
- Gerard Kleywegt7
- Livia Perfetto7
- Nandana Madhusoodanan7
- Piraveen Gopalasingam7
- Tobias Rausch7
- Ugis Sarkans7
- Francesco Iorio6
- Nikiforos Karamanis6
- Patricia Carvajal Lopez6
- Pedro Raposo6
- Sandra Orchard6
- Astrid Gall5
- Attilio Vittorio Vargiu5
- Blake Sweeney5
- Giuliano Malloci5
- James Stephenson5
- Jonathan Manning5
- Julia Foreman5
- Maira Ihsan5
- Pablo Moreno5
- Paul Denny5
- Piv Gopalasingam5
- Sarah Butcher5
- Thawfeek Varusai5
- Alessandra Villa4
- Anton Petrov4
- Aurelien Dugourd4
- Chris Quince4
- Denise Carvalho-Silva4
- Dona Shaju4
- Erin Haskell4
- Evangelia Petsalaki4
- George Georghiou4
- Girolamo Giudice4
- Gun Antonia Nilsson Lock4
- Henning Hermjakob4
- Irene Papatheodorou4
- Johannes Griss4
- Josephine Burgin4
- Juan Antonio Vizcaino4
- Kalpana Paneerselvam4
- Kostas Billis4
- Leanne Haggerty4
- Lennart Martens4
- Liis Kolberg4
- Marton Olbei4
- Mathieu Bourgey4
- Michele Magrane4
- Peter McQuilton4
- Preeti Choudhary4
- Ricard Argelaguet4
- Sara Rocio Chuguransky4
- Simon Andrews4
- Summer Rosonovski4
- Varsha Kale4
- Vera Matser4
- Vytautas Gapsys4
- Yasset Perez-Riverol4
- Yvonne Lussi4
- Zhichao Miao4
- Adam Hospital3
- Adrian Turjanski3
- Alex Mitchell3
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