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- European Bioinformatics Institute (EBI)7
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- 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
- Janick Mathys143
- Bruna Piereck72
- Alexander Botzki68
- Wandrille Duchemin57
- True Colours48
- Christof De Bo45
- Matheus Lourenço42
- Benjamin Pavie38
- Elien Vandermarliere34
- Lieve Ongena34
- Geert van Geest33
- Aleena Mushtaq29
- Robin Engler29
- René Custers23
- Agnes Uhereczky21
- Lucia Smit20
- Amanda Gonçalves19
- Benjamin Moore18
- Gert Van Isterdael18
- Joris De Wolf18
- Niels Vandamme18
- David Armstrong17
- Liesbeth De Milde17
- Michal Szpak17
- Nancy George17
- Robin Lefebvre17
- Deepak Tanwar16
- Birgit Meldal15
- James Collier14
- Kobe Lavaerts14
- Sarah Morgan14
- Tom Hancocks14
- Wendi Bacon14
- Alexandra Holinski13
- Baron Koylass13
- Emily Perry13
- Sebastian Munck13
- Anna Swan12
- Dayane Rodrigues Araujo12
- Sam Holt12
- Tania Wyss Lozano12
- Tatiana Woller12
- David Wishart11
- Kalpana Panneerselvam11
- Lee Larcombe11
- Nicolas Vannieuwkerke11
- Ricard Argelaguet11
- Alex Bateman10
- Alexandre Bonvin10
- Barbara Baert10
- Gustavo Ruiz Buendia10
- Krishna Kumar Tiwari10
- Lorna Richardson10
- Malachi Griffith10
- Niels Geudens10
- Rossana Zaru10
- Veronique Voisin10
- An Staes9
- Bob Asselbergh9
- Frédéric Burdet9
- Gary Bader9
- Jolan Heyse9
- Lennart Martens9
- Lieven Clement9
- Louisse Paola Mirabueno9
- Mathieu Bourgey9
- Obi Griffith9
- Osman Salih9
- Rahuman Sheriff9
- Silvie Fexova9
- Tallulah Andrews9
- Thawfeek Mohamed Varusai9
- Thuong Van Du Tran9
- Typhaine Paysan-Lafosse9
- Ajay Mishra8
- Alexey Larionov8
- Andrew Hercules8
- Bart Mesuere8
- Ben Verhoeven8
- Cath Brooksbank8
- Eliot Ragueneau8
- Evelien Van Hamme8
- Fabio Madeira8
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- Geert Vermaerke8
- Hema Bye-A-Jee8
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- Koen Van den Eeckhout8
- Nicolas Peredo8
- Pavankumar Videm8
- Rachel Marcone8
- Tania Wyss8
- Trevor Pugh8
- Alyona Ivanova7
- Arne Defauw7
- Bart Ghesquière7
- Emily Bowler-Barnett7
- Engy Nasr7
- Gerard Kleywegt7
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