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Biology research is complex. The sample size of a biological data set is often too small to make confident judgments about the biological system being studied.
During Jason Knight’s PhD research, the RNA sequence data that he was studying was not significant enough to make strong conclusions about the gene regulatory networks he was trying to understand.
After working in academia, and then at Human Longevity, Inc Jason came to the conclusion that the best way to work towards biology breakthroughs was to work on the computer systems that enable those breakthroughs. He went to work at Nervana Systems on hardware and software for deep learning. Nervana was subsequently acquired by Intel. In this episode, we discuss how machine learning can be applied to biology today, and how industrial research and development is key to enabling more breakthroughs in the future.
The main lesson I took away from this show is that while we have seen phenomenal breakthroughs in certain areas of health–like image recognition applied to diabetic retinopathy or skin cancer–the challenges of reverse engineering our genome to understand how nucleic acids fit together into humans are still out of reach, and improving the hardware used for deep learning will be necessary to tackle these kinds of informational challenges.
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Thank you, Jeff, for the amazing podcasts. I was wondering why few of your podcasts have transcripts while others don’t have. It surely becomes easier to follow the podcasts — understanding the technical jargons used by the host – with the transcripts available.
Adding show notes for every podcast would also add more value for the listeners who are not conversant to the technology field being discussed. These notes may contain the web links to the libraries/ frameworks the guest is referring to.
Thanks again. You are doing a great job. 🙂