Nima Aghaeepour CIHR Fellow, an ISAC Scholar, and an OCRF Ann Schreiber Investigator at Stanford University
High-throughput Single-cell Biology: the Challenges and Opportunities for Machine Learning Scientists
The immune system does a lot more than killing “foreign†invaders. It’s a powerful sensory system that can detect stress levels, infections, wounds, and even cancer tumors. However, due to the complex interplay between different cell types and signaling pathways, the amount of data produced to characterize all different aspects of the immune system (tens of thousands of genes measured and hundreds of millions of cells, just from a single patient) completely overwhelms existing bioinformatics tools. My laboratory specializes in the development of machine learning techniques that address the unique challenges of high-throughput single-cell immunology. Sharing our lab space with a clinical and an immunological research laboratory, my students and fellows are directly exposed to the real-world challenges and opportunities of bringing machine learning and immunology to the (literal) bedside.
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