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ScaiDigest August 2023

ScaiDigest August 2023

ScaiDigest August 2023

ScaiDigest August 2023

Sukalp Muzumdar, our esteemed expert data scientist, has delved into cutting-edge research once again in the latest edition of ScaiDigest! 

In the August 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 – unraveling 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 modeling 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 tumors. 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.

expiMap – https://www.nature.com/articles/s41556-022-01072-x

MAYA – https://www.nature.com/articles/s41467-023-37410-2

Stay tuned to ScaiDigest as we continue to share fascinating scientific discoveries and their potential impact on the world of healthcare.

About Scailyte

Scailyte is an ETH Zürich spin-off with a best-in-class artificial intelligence platform for the discovery of complex disease patterns from single-cell data. Our solution provides unprecedented insight into the disease and patients’ biology and enables the discovery of new clinically-relevant biomarker signatures by uncovering human’s hidden “single-cell” secrets. 

Scailyte’s proprietary best-in-class data analysis platform ScaiVision™ associates multimodal single-cell datasets (RNA-/TCR-/BCR-seq, proteomics, etc.) with clinical endpoints, such as disease diagnosis, progression, severity, treatment response, and toxicity response to identify ultra-sensitive biomarker signatures and cell functionality states. The performance and clinically-relevant applications of Scailyte’s platform ScaiVision have been demonstrated in well established CAR-T cell therapies and various clinical projects in Oncology and Immunology.

For more information, visit www.scailyte.com and connect on social media @LinkedIn and @Twitter.

ScailyteTM and ScaiVisionTM are registered trademarks proprietary to Scailyte AG.

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