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- European Bioinformatics Institute (EBI)26
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- AI6
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Scientific topic
- Machine learning
- Bioinformatics49
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- Workshops and courses25
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- United Kingdom7
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Target audience
- This course is aimed at advanced PhD students and post-doctoral researchers who are currently working with large-scale omics datasets with the aim of discerning biological function and processes. Ideal applicants should already have some experience (ideally 1-2 years) working with systems biology or related large-scale multi-omics data analyses. Applicants are expected to have a working knowledge of the Linux operating system and the ability to use the command line. Experience of using a programming language (i.e. Python) is highly desirable, and while the course will make use of simple coding or streamlined approaches such as Python notebooks, higher levels of competency will allow participants to focus on the scientific methodologies rather than the practical aspects of coding and how they can be applied in their own research. We recommend these free tutorials: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Python turorial: https://www.w3schools.com/python/ R tutorial: https://www.datacamp.com/courses/free-introduction-to-r Regardless of your current knowledge we encourage successful participants to use these, and other materials, to prepare for attending the course and future work in this area.1
- This course is aimed at scientists working with biomage data across the life sciences. It is suitable for those involved in creating bioimages or taking their first steps in analysis. The content would also be suitable for those wanting to learn more about the BioImage Archive and gain experience with machine learning approaches for image analysis. The programme will be of particular interest to bioimage analysts with questions relating to the use of ‘big data’ and using the wealth of publically available data curated in the BioImage Archive. The course should be accessible to members of the bioimaging community and does not require prior experience with machine learning methods or use of the BioImage Archive. Applicants are encouraged to explore the resources below before starting their application. Applicants should be comfortable with basic programming tasks and have experience working with Python. Prerequisite reading: BioImage Archive: A call for public archives for biological image data ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy The BioStudies database - one stop shop for all data supporting a life sciences study EMPIAR: a public archive for raw electron microscopy image data Image Data Resource: a bioimage data integration and publication platform BioImage Model Zoo 1
- This workshop is aimed at researchers and bioinformaticians from across industry and academia who are looking to leverage machine learning approaches in protein function prediction. It will guide participants through the use of big data to build analytical workflows on publically-available biological data. Participants will require prior experience in the use of the command line interface and confidence in a programming language to fully benefit from the workshop. Please contact us if you have any questions about the course's suitability before you apply.1
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Instructor
- Altuna Akalin1
- Alvis Brazma1
- Andrew Green1
- Andrii Iudin1
- Anna Kreshuk1
- Anne-Laure Boulesteix1
- Aurelien Dugourd1
- Awais Athar1
- Barbara Diaz-Rohrer1
- Beth Cimini1
- Birgit Meldal1
- Callum Tromans-Coia1
- Craig Russell1
- Dave Edwards1
- Dmytro Fishman1
- Dominik Kutra1
- Esti Gomez de Mariscal1
- Evangelia Petsalaki1
- Federica Eduati1
- Frances Wong1
- Gerard Kleywegt1
- Girolamo Giudice1
- Guillame Jacquemet1
- Hermann Zellner1
- Jason Swedlow1
- Javier De Las Rivas1
- Jean Marie Burel1
- Joaquín Dopazo1
- Julio Saez Rodriguez1
- Kalpana Paneerselvam1
- Konrad Förstner1
- Leo Parts1
- Manik Garg1
- Mario Costa Cruz1
- Martin Weigert1
- Matthew Hartley1
- Matthias Blum1
- Melanie Vollmar1
- Nataša Pržulj1
- Osman Salih1
- Paul Korir1
- Petr Walczysko1
- Rabie Saidi1
- Rebecca Senft1
- Sebastian Lobentanzer1
- Simone Weyand1
- Soham Mandal1
- Sriram Sundar Somasundharam1
- Timo Zimmermann1
- Tom Hancocks1
- Tushar Mandloi1
- Ugis Sarkans1
- Virginie Uhlmann1
- Wei Ouyang1
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