81–90 of 175 posts
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I'm one of the main developers of BioJulia. I believe our main issue is lack of developer manpower, and not necessarily lack of funds. Of course, if we got enough money to actually employ a developer, that would be amazing. It's just not very realistic. Furthermore, having BioJulia be developed by working scientists has its advantages. If you, or anyone else, is interested in BioJulia, do think about making a contrib…
Can a programmer with zero knowledge of bioinformatics be of help too? Or do you need a bio background?
Usually, the best packages come about when people are motivated to creating something specific, for example if they think the status quo in some domain is not good enough.
I'm sure we can dig up a handful of old, badly maintained projects that could use some love. Off the top of my head, it would be nice to have
* Micro-optimized our smith-waterman algorithm. That's probably fairly easy to get started with if you're not a bio person
* A number of our parsers have not been properly maintained. We use finite state automata https://github.com/BioJulia/Automa.jl to create parsers. That's for more advanced users
* We need to consolidate our scattered sources of k-mer analysis code. Another developer is re-writing our k-mer iterator protocol, but we need a big toolbox for k-mer counting, minhashing, calculating k-mer spectra etc. That's also very computer-sciency and no so much biological
Feel free to get in touch on the Julia Slack, or send me an email :)
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Can a programmer with zero knowledge of bioinformatics be of help too? Or do you need a bio background?
I'd like to second this question. I'm very interested in bioinformatics as a field, but no background. Would be happy to devote some free time but I wouldn't want to be counterproductive.
https://github.com/ModiaSim/Modia.jl
The authors of that tool have a strong background in modeling and simulation of differential algebraic equations. Not so much in designing DSLs, though, so there maybe some technical oddities. But I expect the simulation aspect to be quite decent.
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A long long long time ago, I wrote this (currently very unmaintained, and I have no interest in maintaining) julia project, don't know if this is useful to you, but it's pretty clear that there is a LOT of potential for julia in this domain, though if I'm not mistaken, the HDL-stuff is one level above what you're doing in your project -- it would be nice if these sorts of things could be composable: https://github.co…
We're not doing design in Julia at the moment, just simulation of existing domains, so we're just reading in netlists from other tools. Eventually I do think Julia would be good at HDL, and I too have an abandoned HDL DSL (that I basically used for an FPGA demo and nothing else), but I don't think HDL is an area where Julia would be able to provide 100x improvements over existing tools (which is about where we want t…
I'm having a lot of trouble understanding how "just add AI" is going to make electronics simulation 1000x faster.
Chris has posted the relevant papers in a sibling comment, but let me try to give an intuitive explanation. Say you have some sort of electronic device like an amplifier. It is made out of many individual transistors and parasitics, but its time evolution is overall guided mostly by the amplifier characteristics its supposed to implement plus boundary effects and corrections from the parasitics. So what you try to do…
1. projections / disregarding some dimension, followed by some model in this latent space, followed by an injection back into the native space. The model is simpler by virtue of operating in a lower dimensional space.
2. using a "reduced-order model" or "surrogate model", but still operating in the same input/output space. The model is simpler by virtue of, e.g. using fewer derivatives or delays.
And with 2, you can have have the simple model e.g. arise from first principles, and learn a _residual_ correction on top of it.
And it sounds like the comment contains a bit of both. Am I getting that right?