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Pumas-AI co-founder here. Indeed, Pharma is conservative due to the impact it can have on patient lives (good or bad). If something is working, it stays, and so
by videep90 6y ago
Pumas-AI co-founder here. Indeed, Pharma is conservative due to the impact it can have on patient lives (good or bad). If something is working, it stays, and something failed them, it gets axed, both people and tools. Having said that, let's give them some credit, as along with the regulators at the FDA, pharma has come a long way from the days of SAS. Even the regulators at the FDA have started gravitating away from SAS into more R-based workflows. While it was a good switch away from SAS, scientists started realizing the bottlenecks with R and the requirement to learn multiple tools to get things working, especially when one has to scale up for the new age analytics. While it is good to have a swiss army knife of tools, there is more overhead in making them work together. I think that is why SAS had shined for the longest time. But as you rightly said, it is cost prohibitive and in the hands of very few.
Pumas and the Julia ecosystem are about integrated modeling speak across multiple sectors that are composable and the best part is that one does not have to develop in one environment and switch to another for production. Your point about Salesforce may be right for trial operations, but someone has to do the anlaytics that connect back to the first principles of physiology and pharmacology. Pumas provides this connection to first principles from the core, and lets you build on it for both internal decision making or to serve the stakeholders with fancy UI's.
A subtle but important point here is that compared to a 30-40 years ago since when SAS became a mainstay, I would argue that more of the younger generation are not averse to coding. In fact programming at the basic level is a fundamental expected skill set, and I personally see more and more life-science majors be efficient coders, thanks to the democratization of tools and education. Combined with this new workforce, and tools like Pumas and Julia, we are not far away from disrupting this sector.
- DrScientist 6y agoWhy Julia? By choosing a language that a tiny minority know and use, haven't you crippled your adoption from the start?
- ViralBShah 6y agoI think Chris Rackauckas answers "Why Julia" below in https://news.ycombinator.com/item?id=24132382 https://news.ycombinator.com/item?id=24132382 This is a very clear case of a well-defined application. The field of pharma needs a combination of mechanistic models (based on differential equations), statistics, and ability to work with large datasets. The existing tools are far from sufficient. If anything, the need for high performance and scalable tools has been felt in the industry for a very long time. There are hundreds of thousands of Julia programmers worldwide now, and the user base is doubling every year. After all, new ideas do start with a tiny minority, but one that truly believes in those ideas and is enthusiastic in adopting them early.
- DrScientist 6y agoThanks - So it's around the speed of certain numerical operations? So you could still use python and just wrap Julia libraries for those operations it's optimized fors
- ChrisRackauckas 6y agoNot necessarily, because the kernel cost can still be too high in many cases. It's not the machine learning case where everything in Python is a matrix multiplication call that will be costly enough to mask the cost of Python. In this cases, users will be writing models that are for scalar operations, and doing things like inlining those functions matters a lot. Of course, you could write something that takes Python code and generates Fortran code that it then statically compiles inline to your other Fortran codes to eliminate all of the overhead, and from that you can see exactly why we resorted to a JIT compiled language. FWIW, other software in this field have essentially built mini JIT compilers for Fortran for doing exactly this, and I'll just say that I'm glad I'm not the one who has to maintain that kind of thing while working on modeling and simulation algorithms.
- DrScientist 6y agoOk - thanks - I guess it depends on the problem - I assume offloading computation to the GPU has similar issues? > FWIW, other software in this field have essentially built mini JIT compilers for Fortran Things like LLVM not specialist enough in terms of optimizations?