Tools for AI/ML research in life sciences is an event series by the SciLifeLab Data Centre aimed at life science researchers who use machine learning methods in their work. The goal of the events in this seminar series is to provide introductions to different tools for ML research but also to foster discussions around our practices and how they can be improved. The events takes place virtually (over Zoom) and are open to researchers in Sweden and beyond. Each event is scheduled for 60 minutes, consisting of a talk and an extended discussion.

Contact: serve@scilifelab.se
Scientific lead: Prof. Ola Spjuth, SciLifeLab Data Centre and Uppsala University

Upcoming events

Oct 19, 10:00-11:00

Using containers to simplify ML training on Berzelius and other supercomputers: beginner-friendly introduction

AI engineers from the SciLifeLab Data Centre and application experts from Berzelius (NSC, LiU).

Abstract: A common approach to train machine learning models is to first create a prototype using a small dataset on a local machine to verify that it works and thereafter use a large scale compute infrastructure such as Berzelius for the full-scale training. One of the challenges with this approach however is incompatible systems in terms of differences in available software packages, versions, etc. An effective way to solve this issue is to use a container solution. Using a container environment allows a highly portable workflow and reproducible results between systems as diverse as a laptop, Berzelius or EuroHPC resources such as LUMI for instance. During this beginner-friendly event, we will introduce and demonstrate how to work with containers on Berzelius (Apptainer and Enroot) using an example from life sciences, starting from raw data and finishing with a trained model. During the Q&A session, the Berzelius life science support team will answer your questions.

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Past events

Sep 08, 10:00-11:00

Modelling life science data in big pharma, academia, and startups – Differences, Examples, and Some Learnings

Andreas Bender
Professor of Molecular Informatics, University of Cambridge

Abstract: Life science data, modelling methods, and organizational environments in which they are implemented vary widely – from big pharma, to academia, and start-up environments (and beyond). This presentation will partly cover science and modelling, but there will be an additional focus on the differences between the above environments (all of which the presenter has worked in), and how they influence the approaches, as well as the eventual result and product to be delivered.