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- Advances in spatial omics5
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- Computational biologists1
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- This course is aimed at bench biologists working in the area of discovery science who want to learn more about bioinformatics tools and resources. No prior knowledge of bioinformatics is required and no experience of programming or the use of Unix / Linux is necessary.1
- This course is intended for master and PhD students, post-docs and staff scientists familiar with different omics data technologies who are interested in applying machine learning to analyse these data. No prior knowledge of Machine Learning concepts and methods is expected nor required1
- 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
- This introductory course is aimed at biologists who are embarking on multiomics projects and computational biologists / bioinformaticians who wish to gain knowledge of the biological challenges 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 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-introduction1
- biocurators1
- bioinformaticians1
- software developers, bioinformaticians1
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Language
- English2
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Instructor
- Sandra Orchard4
- Johannes Griss3
- Lee Larcombe3
- Rachel Lyne3
- Ajay Mishra2
- Asier Gonzalez2
- Denise Carvalho-Silva2
- Dezso Modos2
- Manik Garg2
- Marton Olbei2
- Pablo Porras Millan2
- Ricard Argelaguet2
- Tamas Korcsmáros2
- Tamás Korcsmáros2
- Yasset Perez Riverol2
- Alejandro Brenes Murillo1
- Alex Bateman1
- Andrew Hercules1
- Andrew Jarnuczak1
- Aurelien Dugourd1
- Britta Velten1
- Claire O’Donovan1
- Danila Bredhkin1
- Danish Memon1
- David Fazekas1
- Elena Lukyanova1
- Francesca Ciccarelli1
- Francesco Iorio1
- Gaurhari Dass1
- Girolamo Giudice1
- Gosia Trynka1
- Hanna Najgebauer1
- Helena Cornu1
- Jacques Serizay1
- Konstantinos Tsirigos1
- Livia Perfetto1
- Magnus Øverlie Arntzen1
- Maria Zimmermann1
- Masa Roller1
- Matthew Hall1
- Melissa Burke1
- Michaela Spitzer1
- Mohamed Alibi1
- Nils Eling1
- Samuel Collombet1
- Sarah Morgan1
- Sergio Contrino1
- Shila Ghazanfar1
- Swee Hoe Ong1
- Tatsuya Nobori1
- Vy Nguyen1
- Yasset Perez-Riverol1
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