Thursday, December 2nd 2021 (17:00 - 18:00)



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Software is a key component of deep-sequencing and point-of-care tools. Smart software solutions allow to speed up the sample-to-answer time, and to save on the system’s power. The importance of software – in the form of machine learning algorithms and artificial intelligence – will only become more prominent in the future when the amounts of data will explode due to the emergence of proteomics, metabolomics, single-cell sequencing, etc. and when millions of sequencing tests will be performed in the daily practice of hospitals worldwide as the cornerstone of their personalized medicine practice.  

Imec has set up a lab, the ExaScience Life Lab, that focuses on software solutions for data-intensive and high-performance computing problems in life sciences. As part of that endeavour the lab developed open-source tools for speeding up the DNA reconstruction and variant calling process as well as very fast single-cell RNAseq software.

Another challenge that the lab is tackling is privacy- and ownership-preserving solutions for machine learning and genomic analysis. In this webinar, we will explain how such amalgamated machine learning could enable a form of federated analytics across multiple parties such that each party keeps its own data and analytics private yet benefits from others by only sharing non-sensitive derived model information.