April 13-14, 2021 – The NHGRI Genomic Data Science Working Group hosted Machine Learning in Genomics: Tools, Resources, Clinical Applications and Ethics. This virtual workshop highlights the opportunities and obstacles that occur when applying machine learning methods to basic genome sciences and genomic medicine. Workshop Agenda: https://www.genome.gov/event-calendar/Machine-Learning-in-Genomics-Tools-Resources-Clinical-Applications-and-Ethics ————— Welcome ————— Co-chairs: Trey Ideker, Ph.D., University of California San Diego Mark Craven, Ph.D., University of Wisconsin Speaker: Eric Green, M.D., Ph.D., Director, National Human Genome Research Institute ————– Keynote: ————– Moderator: Shannon McWeeney, Ph.D., Oregon Health and Sciences University Presenters: Eric Topol, M.D., Scripps Research Brad Malin, Ph.D., Vanderbilt University Medical Center Chapters: 0:00 – Start 0:09 – Welcome (Mark Craven) 0:52 – Welcome (Trey Ideker) 1:28 – Opening Remarks (Eric Green) 9:41 – Keynote Presentation (Eric Topol) 34:54 – Keynote Presentation (Brad Malin) 59:00 – Q&A Session with Shannon McWeeney, Eric Topol and Brad Malin
Mathias Ekman from Microsoft speaks about the future of Life science powered by AI and Genomics. #pharma #ai #science #genomics #conference #dubrovnik #pharmaceuticals #panel #future #learning #talks #keynote Get your tickets for this NEXT 2021 event now: https://nextpharmasummit.com
I’ll be giving the talk below to an audience of oligarchs in Los Angeles next week. This is a video version I made for fun. It cuts off at 17min even though the whole talk is ~25min, because my team noticed that I gave away some sensitive information 🙁 Slides: https://drive.google.com/open?id=1NcsgvWUKBmlr_qcgL0VXN5H0JVmsJDdJ A Brief History of the (Near) Future: How AI and Genomics Will Change What It Means To Be Human AI and Genomics are almost certain to have huge impacts on markets, health, society, and even what it means to be human. These are not two independent trends; they interact in important ways, as I will explain. Computers now outperform humans on most narrowly-defined tasks, such as face recognition, voice recognition, Chess, and Go. Using AI methods in genomic prediction, we can (for example) estimate the height of a human based on DNA alone, plus or minus an inch. Almost a million babies are born each year via IVF, and it is possible now to make nontrivial predictions about them (even, about their cognitive ability) from embryo genotyping. I will describe how AI, Genomics, and AI+Genomics will evolve in the coming decades. Short Bio: Stephen Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech.
SKCET – ATAL FDP on Artificial Intelligence, Machine Learning and Genomics – Session 8
SKCET – ATAL FDP on Artificial Intelligence, Machine Learning and Genomics – Session 11
SKCET – ATAL FDP on Artificial Intelligence, Machine Learning and Genomics – Session 10
As technology continues to develop our society faces pressing decisions: Who needs to know what makes you, you? Colby Deweese is a Senior chemical engineer at the University of Tulsa. For the past year he has worked with statistical genomics at Oklahoma Medical Research Foundation. He was recently accepted to pursue his PhD in computational biology and bio informatics at Yale. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Machine learning and artificial intelligence are changing the nature of biological research, especially genomics. Artificial intelligence applications are opening up our understanding of ourselves and disease, and we must strive to create tools that can work as partners in research, not simply as black boxes. Barbara Engelhardt is an assistant professor in the Computer Science Department at Princeton University since 2014. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution, an NIH NHGRI K99/R00 Pathway to Independence Award, and the Sloan Faculty Fellowship. Professor Engelhardt is currently a PI on the Genotype-Tissue Expression (GTEx) Consortium. Her research interests involve statistical models and methods for analysis of high-dimensional data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human diseases. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx