Alexander Bernier BCL, JD, LLM, SJD (Candidate) is a Montreal-based lawyer, and a doctoral candidate at the University of Toronto Faculty of Law. His work aims to give scientists a range of compliant data sharing designs that scientists can implement in different situations.

In this episode, we discuss the risk of identifying individuals in a biological data set, how this is approached differently in different countries, and possible strategies to ensure data privacy.

The episode is hosted by Dr. Ulrik Stervbo and Dr. Zhaoqing Ding.

Comments are welcome to the inbox of onairr@airr-community.org  or on social media under the tag #onAIRR. Further information can be found here: https://www.antibodysociety.org/the-airr-community/airr-c-podcast. 

Maxim Zaslavsky is a computer scientist using machine learning to address problems in immunology. He is currently PhD student at Stanford University.

Scott D. Boyd is a physician-scientist and Professor of Pathology and of Food Allergy and Immunology at Stanford University. His group is focused on using high-throughput DNA sequencing and single-cell experiments to analyse human immune responses to infection and vaccination.

We discuss the preprint “Disease diagnostics using machine learning of immune receptors”, available at BioRxiv: https://doi.org/10.1101/2022.04.26.489314. The work is led by Maxim Zaslavsky with Scott Boyd the corresponding author. In the manuscript, the authors demonstrate how AIRR-seq and machine learning can be used in disease diagnostics.

The episode is hosted by Dr. Ulrik Stervbo and Dr. Zhaoqing Ding.

Comments are welcome to the inbox of onairr@airr-community.org  or on social media under the tag #onAIRR. Further information can be found here: https://www.antibodysociety.org/the-airr-community/airr-c-podcast.