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E46: Why is AI essential for developing new medicines for genetic diseases?

Catch the Ask AI interview with Amit Deshwar, VP and Head of Predictive Systems at Deep Genomics, the AI Workbench used to prioritize transformational RNA therapeutics for genetic diseases

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Amit Deshwar is the Vice President and Head of Predictive Systems at Deep Genomics. Under his leadership, Deep Genomics has developed more than 40 machine learning models of molecular phenotype and developed novel GalNAc hepatocyte delivery technology. Recently his team has released the AI Workbench 2.0, that fuses deep mechanistic understanding of SBO activity, predictive models and high throughput sequencing datasets that enables rapid development of expression increase SBOs.

Prior to Deep Genomics, he worked at Google where he built a highly distributed log parsing system and power supply firmware that enabled co-ordinated reductions in peak power consumption. In addition, he founded a startup that matches employers and potential employees using predictive models of personality and workplace attributes.

Amit received bachelor degrees in Software Engineering and Psychology from the University of Calgary. His Phd is from the University of Toronto, where he studied machine learning for oncology, and built tree-structured Baysesian nonparametric models for untangling the complex biology of intra-tumour heterogeneity.

Tune in to this Ask AI interview host Melissa Kargianakkis meets Amit Deshwar, VP and Head of Predictive Systems at Deep Genomics, to ask the question:

Why is AI essential for developing transformative medicines for genetic diseases?

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