ScaiDigest Volume 2
?????Sukalp Muzumdar, PhD?Muzumdar, our esteemed expert data scientist, has delved into cutting-edge research once again in the latest edition of ScaiDigest! ??
In the second edition, Sukalp explores the critical aspect of model interpretability in the application of artificial intelligence in the biological sciences.
“Thrilled to post the second edition of ScaiDigest – unravelling the cutting-edge research in our field! Today I’d like to highlight 2 recent publications on model interpretability –
Model interpretability is a major bottleneck in the adoption of artificial intelligence in the biological sciences. The ability to understand the reasoning behind model predictions is of crucial importance for translation to the bedside, as well as to build trust amongst biologists and clinicians.
I’d like to share two recent articles which demonstrate methods explicitly incorporating prior biological knowledge into the modelling and analysis of single-cell sequencing data – expiMap from Lotfollahi et al, and MAYA from Landais and Vallot.
While expiMap constructs a directly biologically-interpretable latent space based on both pre-defined and de novo-identified gene programs, MAYA enables the in-depth analysis of pathway activation allowing for the discovery of shared activation modes across cell populations and tumours. These tools are pushing the frontiers in the use of machine learning in biology, driving a shift towards the democratization of biological insights.
Having a background in both data science and biology, these two pieces particularly resonated with me, and I am excited by their ability to bring tangible biological insights from complex data such as single-cell RNA sequencing enabling everyone to peer inside the conventional black box of artificial intelligence.
Stay tuned to ScaiDigest as we continue to share fascinating scientific discoveries and their potential impact on the world of healthcare.