Data Science Summary -- 3

Need 10 summaries for the following 10 podcasts on data science. Word limit 80-100 words per summary. Just write one or two paragraph at max per summary. 1) Disease Network Modelling, Mixture Models, and Career Opportunities at SDSS 2020 Dave Hunter highlight a variety of cool life science collaborations he has worked on, including the network models used to describe AIDS transmissions and mixture modelling to describe pediatric cognitive tests. We then talk about the upcoming SDSS 2020 conference, and its newest additions to benefit early career researchers. [login to view URL] 2) Risks and Opportunities of AI in Clinical Drug Development Risks and Opportunities of AI in Clinical Drug Development. There are many places in which ML/AI methods can be of benefit to pharmaceutical research (several have already been covered on the show). David and Demissie explain where AI can fit in to in vivo studies, which carries it’s own benefits, but also with heightened risk to to human test subjects. They go on to cover several other areas of interest including AI for observation studies and real world evidence. It’s a “big tent” conversation as we lead up to the Pfizer/ASA/Columbia University Symposium on Risks and Opportunities of AI in Clinical Drug Development. [login to view URL] 3) AmsterdamUMCdb, Europe’s first open ICU database It’s not everyday that medical researchers give the world access to 13+ years of dense, high-quality critical care data. Intensivist Paul Elbers describes the data set along with the clinical priorities in collecting the data. Paul covers a range of topics including protecting the patients’ interests and anonymity, a clinician’s priorities when selecting clinical performance metrics, and the stages of validating predictive algorithms up to the stage of an RTC. The work done to create AmsterdamUMCdb is an incredible feat and a huge boon to the medical science profession. [login to view URL] 4) Machine Learning and Mathematical Modeling of Wound Healing - I John discusses his work in the precision medicine program at the Statistical and Applied Mathematical Sciences Institute (SAMSI) to model wound healing. He describes the physiological mechanisms of wound healing and how to select a applications that are appropriate for mathematical modelling. [login to view URL] 5) Machine Learning and Mathematical Modeling of Wound Healing - II John is back to show how machine learning can vastly speed up the selection of mathematical models. His presentation provides great visual intuition on how machine learning methods can help select mathematical models, even as measurement noise increases. It’s a huge improvement over selecting models by hand! [login to view URL] 6) Big Data Squared - Combining Brain Imaging and Genomics for Alzheimer’s Studies - I [login to view URL] 7) Big Data Squared - Combining Brain Imaging and Genomics for Alzheimer’s Studies - II [login to view URL] 8) High Performance Computing for Data Science High Performance Computing for Data Science [login to view URL] 9) Building a Knowledge Graph Database with Julia Building a Knowledge Graph Database with Julia [login to view URL] 10) Early Career Services for Statisticians and Data Scientists Early Career Services for Statisticians and Data Scientists [login to view URL]