This course type is computational

Systems Biology: From Large Datasets to Biological Insight

06 - 10 July 2020Wellcome Genome Campus, UK
Deadlines (at 23:59 UTC):
  • Application and bursary deadline 08 April 2020
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Hands-on training in using large-scale multi-omics data and machine learning to infer biological models

  • Summary

    This popular course, run jointly with EMBL-EBI, covers the use of multi-omics data and methodologies in systems biology. The content will cover a range of approaches – ranging from network inference to machine learning – that can be used to extract biological insights from varied data types. Together these techniques will provide participants with a useful toolkit for designing new strategies to extract relevant information and understanding from large-scale biological data.

    The motivation for running this course is a result of advances in computer science and high-performance computing that have led to ground-breaking developments in systems biology model inference. With the comparable increase of publicly-available, large-scale biological data, the challenge now lies in interpreting them in a biologically valuable manner. Likewise, machine learning approaches are making a significant impact in our analysis of large omics datasets and the extraction of useful biological knowledge.

    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 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.

    Numerous free online tutorial are available for the resources used in the course, including:

    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

  • Programme

    The programme will include, lectures, discussions and practical computational exercises covering the following topics:

    • Data reduction and data integration methods – including comparison of major approaches through lectures and practical exercises
    • Machine and deep learning – practical exercises on supervised machine learning, including classification and regression, and deep learning
    • Functional inference from omics data – approaches to extract signatures of cell state from -omics data including transcription factor activation and kinase activity states. Extraction of upstream signalling pathways from transcriptomics datasets
    • Network inference and signal propagation – network inference approaches from -omics data
    • Group projects – including the use of diffusion-based approaches to predict signal propagation and identify cell activity signatures
    • Introduction to executable modelling – including how to fit omics data to executable and predictive logic models

     

    Learning Outcomes
    After the course you should be able to:

    • Discuss and apply a range of data integration and reduction approaches for large scale omics data
    • Apply different approaches to explore omics data at the network level
    • Describe principles behind different machine learning methods and apply them on omics datasets to extract biological knowledge
    • Infer biological models using statistical methods
    • Identify strengths and weaknesses of different inference approaches
    • Compare signal propagation through logic modelling vs diffusion-based approaches
  • Instructors and speakers

    Lead instructors/organisers

    Konrad Förstner

    Konrad Förstner
    TH Köln University of Applied Sciences & ZB MED Information Centre for Life Sciences, Germany

    Federica Eduati

    Federica Eduati
    Eindhoven University, Netherlands

    Tom Hancocks

    Tom Hancocks
    EMBL-EBI, UK

  • Cost and bursaries
    Cost Accommodation / meals
    *Course fee £750 This is a residential course and the fee includes all accommodation and meals.

    *The course fee is subsidised by Wellcome Genome Campus Advanced Courses and Scientific Conferences and applies to non-commercial applicants. Please contact us for the commercial fee.

    Bursaries
    Limited bursaries are available (up to 50% reduction on the course fee) and are awarded on merit. If you would like to apply for a bursary, please complete the bursary section of the online application form.

    Where there are many bursary applications, the selection committee may issue smaller amounts.

    Bursaries can be applied for as part of the course application form. Applicants will be notified of a bursary award along with their place on the course, usually within one month of the application deadline. The decision of the selection committee is final.

    Please note that both the applicant and sponsor are required to provide a justification for the bursary as part of the application.

    Additional funding opportunities
    Visit our support page for additional financial support currently available.


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  • How to apply

    Prerequisites
    Applicants should be

    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 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.

    Numerous free online tutorial are available for the resources used in the course, including:

    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

    How to Apply
    Please click the Apply button above to begin the online application process. Places are limited and will be awarded on merit. If you have any problems with the online application process, please contact us.

    Please note: Applications must be supported by a recommendation from a scientific or clinical sponsor (e.g. supervisor, line manager or head of department). A request for a supporting statement will be sent to your nominated sponsor automatically during the application process. Applicants must ensure that their sponsor provides this supporting statement by the application deadline. Applications without a supporting statement cannot be considered.

    Travel visas
    Successful applicants will be provided with a support letter for their visa application, if required.

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