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Deep Learning in Biomedicine and Genomics: An Introduction and Applications to Next-generation Sequencing and Disease Diagnostics
December 4, 2017 @ 6:30 pm - 8:30 pm PST
- We will review the history and taxonomy of machine learning and artificial intelligence
- We will introduce deep learning, covering both what it is and why its so exciting.
- We will review a highlight a few deep learning applications to biomedical problems across the field
- We will do a deep dive into three recent deep learning applications from Google Brain:
- Detection of cancer cells in pathology images
- Detection of diabetic retinopathy from fundus images of the eye
- Calling SNP and indel variants in next-generation sequencing data
Biography: I’m Mark DePristo, a Google software engineer since 2015. I lead the Google Brain Genomics team where we work on advancing the capabilities and applications of deep learning tech in TensorFlow for genomics problems. Before joining Google I was Vice President of Informatics at SynapDx, a Google Ventures-backed startup developing a blood-based test for Autism. And before that I was Co-Director of Medical and Population Genetics at the Broad Institute where I created and led the ~10 person team that developed the GATK, the dominant software for processing next-generation DNA sequencing data. I have an BA in Computer Science and Math from Northwestern, a PhD in Biochemistry from University of Cambridge where I was a Marshall fellow, and finally postdoc’d at Harvard to study antibiotic resistance evolution. My academic articles are widely published with more than 28,000 citations.