On 15 & 16 October, there is an online demo/hands-on session and online Q&A on Using MOFA for integration of Omics Data.

In the era of Big Data, the tsunami of massive ‘omics’ data is revolutionizing the way we do science. Life science researchers are no longer analyzing one data set at a time but are moving towards multi-disciplinary integrative biology. It has been demonstrated that integration of different ‘omics’ data types (such as on genomes, transcriptomes, proteomes, epigenomes, etc..), boosts biological discoveries and improves predictions of the underlying interactions and regulation among molecular entities. Integrating different ‘omics’ datasets is a challenging task that relies heavily on data mining and machine learning algorithms. One must account for the specificities of each data type, solve problems associated with processing data across different platforms, and take into account the variable reliability levels of heterogeneous data.

In the second edition of this training on Multi-Omics data integration, four different topics will be addressed: ‘Multi-Omics Factor Analysis’, ‘Single Cell Data Analysis’, ‘Machine Learning, Deep Learning & Genomics/Proteomics’, and ‘Machine Learning in Drug Discovery and Disease’. We have invited top level speakers to share their insights with you on the latest developments in the field.

This course is organized in collaboration with Helis Academy. More information see https://helisacademy.com/

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