Content provider: VIB Bioinformatics Core
Mass spectrometry data processing - postponed
2 - 3 June 2020
Gent, BelgiumMass spectrometry data processing - postponed https://training.vib.be/product/135 https://tess.elixir-europe.org/events/mass-spectrometry-data-processing-f80a1c34-fcd4-40bf-83df-1ba4b05dd38e Obtain a good understanding of the origins and properties of mass spec data Obtain an understanding of the processing of mass spec data, aimed at identifying and quantifying peptides and proteins Gain sufficient understanding of the software tools and database used, and of the issues and caveats involved, to critically analyse and assess results from mass spectrometry based proteomics experiments 2020-06-02 09:00:00 UTC 2020-06-03 00:00:00 UTC VIB Bioinformatics Core iGent, Gent, Belgium iGent Gent Belgium 9052      
Using MOFA for integration of omics data - postponed
9 June 2020
Gent, BelgiumUsing MOFA for integration of omics data - postponed https://training.vib.be/product/151 https://tess.elixir-europe.org/events/using-mofa-for-integration-of-omics-data-f849603e-3ab6-453b-b6d4-6d22f3666d6e Participants can bring their own data to the course. What kind of preprocessing of the data is required for MOFA? How to train MOFA on a multi-omic data set? How to interpret the MOFA factors by their loadings, using gene set enrichment or sample ordination? How to use MOFA for downstream analyses including regression, classification or clustering? How to impute missing values with MOFA? How to select the number of factors and compare different MOFA fits? 2020-06-09 09:00:00 UTC 2020-06-09 00:00:00 UTC VIB Bioinformatics Core iGent, Gent, Belgium iGent Gent Belgium 9052      
A tour of machine learning: classification
31 August - 1 September 2020
Gent, BelgiumA tour of machine learning: classification https://training.vib.be/product/203 https://tess.elixir-europe.org/events/a-tour-of-machine-learning-classification-67c1e87f-922a-469a-8aaa-f93f3b51b01b Machine learning has become ubiquitous in biotechnology (as in many other fields), fueled largely by the increasing availability and amount of data. Learning algorithms can figure out how to perform important tasks by generalizing examples. Typical applications are diagnoses/prognoses, gene/protein annotation, drug design, image recognition, text mining and many others. However, building successful machine learning models requires a substantial amount of “black art” that is hard to find in textbooks. This course is an interactive Jupyter Notebook (Python) that will teach you how to build successful machine learning models. No background in machine learning is assumed, just a keen interest. 2020-08-31 09:00:00 UTC 2020-09-01 00:00:00 UTC VIB Bioinformatics Core iGent, Gent, Belgium iGent Gent Belgium 9052      
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