Genomics
topic on 3 shows · 14 statements across 12 episodes
the Y Combinator Startup Podcast 
the a16z Podcast 
20VC
14 statements about Genomics, every show
Pande: Genomics generated less actionable signal than precision medicine expected
“And I think genomics had signal, but not as much signal as I think people were hoping for.”
Clark: The neurotech market unlock will rival the scale of genomics
“When we start to understand genes, to this day, we're still learning how to take advantage of that knowledge day to day in the medical industry, in the medical research industry, and the same thing will come for the brain.”
Pande: Genomic diagnostics scale across cancer types via repeatable engineering
“Science is something that you can't schedule creativity or the ability to come through a breakthrough. So if you have understood the science of colorectal cancer, what you've learned about a stool test probably is not going to be very useful for a breast cance…”
Pande: Gap between genomics and health risk remains large due to environment
“So the big problem is that it's a long gap between genomics to risk, and actually the environmental factors are also so key.”
Walaliate: Genomics has few true healthcare applications 20 years after HGP
“We certainly haven't lived up to that, and in fact, it's been a little difficult to see exactly where genomics has had a true application today.”
Araya: Existing genomic maps lack the functional data needed for applications
“Unfortunately the maps that we have today are really maps of function that just say where things that are, things like genes, biomolecules, where they are encoded in the genome, but it says really nothing about how they function and which parts of the genes do…”
Kaditz: Genomic disease prediction requires time-series biomarker data
“I think in order for genomics to be used in diagnostics or predictive models of are you going to get sick, I think it has to be combined with actual time series biomarker data”
Walaliyadde: Genomics splits into sequencing hardware like Intel and clinical applications
“And an easy way to think about genomics is there's a sequencing layer, which is the companies that make the sequencing machines like Illumina, which is like sort of Intel and chips. And then there's the application layer, which are companies that are using thi…”
Pandey: Exponential compute and genomic cost declines make health tech transformation inevitable
“The, there's almost like a deterministic nature of Moore's law, that the cost of compute and cost of genomics and these things decreasing exponentially. That actually is something that's really hard to avoid, and so in my mind, it's not really a question of if…”
Regulatory barriers delay hard tech compared to consumer software
“There are applications in genomics and, you know, drones and Bitcoin and three printing and things like that that do run into regulatory challenges. And so you know, some of the reason that people see these things that are perceived as fluff that, that, you kn…”
Gil: Data moats rarely succeed as standalone startup assets outside genomics
“Yeah, the most common miracle that's quoted today is a data moat. People say, well, we'll generate tons of data, and then we'll be differentiated, and that, I think in genomics something like that could work, but outside of that, I actually think I've never se…”
Gil: Data moats do not exist in most industries
“There are very, very few companies that actually have data moats, and so I think that's often a red herring. I think maybe in genomics right now there may be interesting data moats to be formed, but in most industries data moats don't really exist.”
Pande: Freenome's genomic ML will drive cancer cure rates to 80-100%
“So a company like Freenome will use machine learning and deep learning with genomics to be able to tell whether you have cancer early enough, such that the cure rate, success rate could be 80%, a hundred percent.”