The biggest pain point I see in this market is that experimental parallelization is an intervention-heavy process.
The biggest pain point I see in this market is that experimental parallelization is an intervention-heavy process. Ignoring the equipment costs, it's also capital-intensive (unlike say deploying to AWS). And finally, obtaining usable data still requires experiential knowledge. There's something about knowing and feeling the data (yeah, that's awfully fuzzy) that is still an important part about obtaining good results [0]. So for any biologic process that is parallelizing the operators are going to want to own the machines anyways. In order to capture a real market, you're going to have to figure out a way to offer parallelization services - be given a non-parallel experiment with certain parameters and scale it up on behalf of the users. So, the user has an experimental plan and just 'hands it over' to transcriptic. I still worry about the experiential knowledge part, putting the experimenter one step away from the experiment is potentially counterproductive. [0] http://onlinelibrary.wiley.com/doi/10.1002/pro.2339/full In this paper, the grad student (and lead author) who had spent four years of her graduate work on a previous paper had a nagging feeling that the data were strange. By actually looking at the wells, she figured out post-publication (with nothing to gain) that the protein was sticking to sides of the 96-well plate and causing the observational data to be artefactual. Then there was the question of how to do more experiments to prove that was going on. And then the political problem of convincing her grad advisor to publish a retraction (well at least it was a retraction worth a 10 page paper and a new citation. The story has a happy ending; she got a position at a pharma company largely on the back of her due diligence).
Posted by dnautics on Dec 8, 2014, 8:29 PM
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> Because they don't believe the disasters were inflicted by them.
Hehe, yeah I'm not sure when John Stewart would have worked there or what the pain points were for him, but he worked there for a bit.
https://www.apple.com/shop/product/mxcl3ll/a/magic-keyboard-...
If first-order hybrids made up only a small proportion of the total population of humans and Neanderthals, then the probability that a pa...
Well, I'd be interested to hear what some of those nuances are, personally.
My biggest Amazon annoyance.
> There's not really an unmet need for innovation in broadband delivery; the issue is running new wires broadband has costs, and in a lot...
Speaking of Amazon's account process, I have a really annoying problem with theirs.
The biggest pain point I see in this market is that experimental parallelization is an intervention-heavy process.
The biggest pain point I see in this market is that experimental parallelization is an intervention-heavy process. Ignoring the equipment costs, it's also capital-intensive (unlike say deploying to AWS). And finally, obtaining usable data still requires experiential knowledge. There's something about knowing and feeling the data (yeah, that's awfully fuzzy) that is still an important part about obtaining good results [0]. So for any biologic process that is parallelizing the operators are going to want to own the machines anyways. In order to capture a real market, you're going to have to figure out a way to offer parallelization services - be given a non-parallel experiment with certain parameters and scale it up on behalf of the users. So, the user has an experimental plan and just 'hands it over' to transcriptic. I still worry about the experiential knowledge part, putting the experimenter one step away from the experiment is potentially counterproductive. [0] http://onlinelibrary.wiley.com/doi/10.1002/pro.2339/full In this paper, the grad student (and lead author) who had spent four years of her graduate work on a previous paper had a nagging feeling that the data were strange. By actually looking at the wells, she figured out post-publication (with nothing to gain) that the protein was sticking to sides of the 96-well plate and causing the observational data to be artefactual. Then there was the question of how to do more experiments to prove that was going on. And then the political problem of convincing her grad advisor to publish a retraction (well at least it was a retraction worth a 10 page paper and a new citation. The story has a happy ending; she got a position at a pharma company largely on the back of her due diligence).
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