L) Breakout sessions May 16


The following breakout sessions will be organised in parallel on May 16 from 13:30 -14:30h.

L1) BIUP industry meeting: Commercial applications of deep learning

Organizers: Antoine Janssen (Keygene), Emiel Ver Loren van Themaat (DSM), Walter Pifovano (Baseclear)

Target group: company representatives & researchers


Niek Bouman, Keygene NV: Industrial applications of deel learning in crop and trait development

Second speaker to be announced.

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L2) Phenotype data capture

Organizers: Jildau Bouwman (TNO), Mariska Bierkens (NKI), Lars Eijssen (Maastricht University), Chris Evelo (Maastricht University), Kees van Bochove (The Hyve)

Target group: Bioinformaticians and biologists

Objective: We would like to discuss the needs and availability of systems that facilitate reuse of phenotypic data.

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L3) Quantitative Immunology

Organizers: Aridaman Pandit (Utrecht University), Jose Borghans (UMC Utrecht) and Rob J de Boer (Utrecht University)

Target group: Computational biologists working in Immunology

Objective: In this workshop, we aim to bring together scientists from different parts of Netherlands to discuss cutting-edge research and future directions in the field of quantitative immunology.

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L4) Models of life: ISBE in action

Organizers: Hans Westerhoff (Vrije Universiteit), Alexey Kolodkin (CEO ISBE NL), Vitor Martins dos Santos (Wageningen University & Research)

Target group: systems biology researchers

Objective: to tell people where we are with ISBE in action and how we propose to populate it further wit services provided by BioSB members.

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L5) 60-minute FAIRification of a data set

Organizers: Daphne van Beek (UMC Utrecht)

Target group: Bioinformaticians/programmers that might be interested in how to easily transform datasets to a FAIR dataset. An example dataset might be chosen that showcases this (for example, show that it is possible to add an additional step to your pipeline that transforms your output file to a FAIR linked data file.

Objective: demonstration of how to make a dataset FAIR: with a focus on applying the data model on the file.

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