So, hey, and I guess you get to introduce yourself.
Absolutely, hey, everybody.
My name is Ben Busby.
I'm going to try to cram three lightning talks into a talk.
First, I'm going to tell you about some cool stuff in videos building the bioinformatics
space.
Second, I'm going to talk about what would happen if you wanted to sequence and distribute
millions of genomes.
And then third, if any of you are interested in knowledge graphs or rag, I'm going to talk
about cramming knowledge graphs and rag to get it.
Everybody good with that?
All right, let's roll.
So yeah, I'm Ben Busby here, some disclosures, but I will say that a lot of us, with a bunch
of academic institutions and stuff, but a lot of us, and in video are really from the open
source community.
So, part of it, I remembered my galaxy t-shirt, but I did forget my NFC or socks.
Anyway, so yeah, we work at an Nvidia, we work with pretty much everyone in the sort of
industrial bioinformatics ecosystem, and also hundreds and hundreds of academic institutions,
really trying to help speed up algorithms and bioinformatics.
It's one of the main things we do, and really across the entire sort of biological science
as ecosystem, it's not shown here, but including agriculture.
I'll talk about a few of these things, but today I'm not going to talk about robots, if
you want to talk about robots after we can do that.
Anyway, so yeah, Nvidia builds all kinds of stuff and also accelerates all kinds of open
source algorithms so you can use them faster and faster and then cramps them together
in convenient ways.
So that's something to think about, but for those of you who are bioinformaticians,
there's about 20 massively sped up regular bioinformatics algorithms.
Here are about 10 of them, and if you're interested, check out Parabrics.
By the way, some of them are in NF Coral, ready, and others are in galaxy.
So that's a really nice thing that I'll talk a little bit about some things we're doing
in single cell, as well as models in a minute.
But if you're a bioinformatician, you want to go faster, Google for Parabrics.
What do I mean by massively faster?
So on an RTX Pro 6,000, BWA is about 135 times faster than it is on a relatively comparable
CPU, so there's a lot of speed up which actually turns into being cost savings if you're
sequencing or mapping a lot of genomes.
So for single cell, things are even faster, so the average single cell workflows about
200 times faster on GPU, you can check it out, and so that's to me that's really, really
cool.
So if you're interested in millions of cells or particularly hundreds of millions of
cells, GPU really just makes sense.
One of the really cool things about this is this all works in Jupiter notebooks too,
so you don't have to know Kudai anything like that, you can just run it.
And then also a lot of people don't know that there's accelerated data science.
If you Google Rapids.ai, there's a pandas, there's a numpy, and I think most importantly,
there's a psychic learn version called QML.
In addition, in addition, in addition, in addition, in addition, in addition, in addition,
in addition, in addition, in addition, in addition, in addition, in addition, in addition,
in addition, in addition, in addition, in addition, in addition, in addition, in addition, in addition,
I think I'm more or less on time, but the vision is that, you know, in five years for people
with very common ideologies of disease, we can really move towards hitting them pharmacology
more or less straight off the sequencer. But I think something we ignore all the time, we think
about variants of this variants that variant disease blah, blah, blah, but, you know, I mean,
about 8% of variants have single disease, single variant associations, according to NHGRI,
and other places, the vast majority of diseases, multi-factorial.
Variant annotation is very common, there's 300 variant annotators, in this tool called open
provide, but still we're missing a lot, we have a lot of variants of unknown significance, why,
probably because of lack of contextualization, because there's actually polygenic.
So here's a bunch of reasons that diseases are multi-factorial, but why don't we think about this
very well? Well, it turns out humans don't like to think about multi-factorial causation.
Deep learning, fortunately, does not have this problem. So, but anyway, so we decided to try to
attack this problem head on in a very, very simplistic way. Some of these things, thank you.
Some of these things are seem quite complicated, but we thought we could do it simply,
and by me, I mean, me and a bunch of really clever graduate students at CMU,
we downloaded a thousand genomes split them by habitat blocks, or sorry,
recombination sites into habitat blocks, and clustered them. What we saw is in Puerto
Reakins, Great Britain's and Humb Chinese, around TNF, you get 9, 8, and 3 clusters of
habitat blocks. Looking at basic ancestry, HLA, you get 17, 15, 13. What does this mean?
This means that genome is discretizable. So basically what we can do is we can go from a linear
genome to a pan genome, but we can make this pan genome without making millions and millions
of alignments, which I think is really important, because that is actually computationally heavy,
even on a GPU. So basically what we're doing is approximate hashing coding for very large genomics
by using these haploblocks. So we go basically from genome graphs to approximate hashing coding,
then what we can do is label these haploblock clusters with snips, and then use them
use convert them to binary strings, and then use these to come up with feature weights for different
parts of the genome in a particular disease of phenotype. So that's something we're particularly
excited about at this particular point. And I would like to note that a bunch of this was actually
built in the Elixir biohagathon in Berlin, in November. So these are a bunch of things that
Nvidia does, but I want to talk about actually knowledge graphs, and that's going to be sort of
the third thing we talk about. So really thinking about the phenotype side of clustering of disease
subtypes, it would be really nice if we could do disease clustering. Why? Because we've known
all the ideologies of breast cancer for 30 years, or the four major ideologies I should say.
But this is what a snowmed graph looks like right now. This is what a graph could look like.
So there is a future there. And I think something I call pig rag could be part of the future
in full disclosure. I didn't do any of the engineering on this project. I just made this graphic,
and I did that with Google Gemini. So not all that much. But anyway, so yeah, I mean, most of you know
what graph rag is, anybody not know what graph rag is? It's written up there. So anyway,
great. So basically what we've done here to do Retrieval Augmented Generation for an LLM.
A lot of people do that now. But basically what we've done is put a pie towards geometric
gen in in front of the LLM. And you can retrieve a subgraph. You can see who's made what contribution,
et cetera, et cetera. And then you can concatenate the embeddings from a gen and from an LLM.
Why is that important? Because then you can also do document retrieval. So you can go ahead and
merge unstructured data with structured data. But the really cool thing here is you can put
out another knowledge graph so you can actually do validation as well as asking natural language
queries. We're doing a lot of experiments where we look at you know one hop to five hop prompting
and bin it and so on and so forth. Anyway, and it's you do need to do some fun tuning of the
decoder. But we tested this out with Neo4j. You can check out this blog. And this was in the
start prime data set. And you can actually you actually double the accuracy of retrieval
augmented generation by putting a gen in front of the LLM. So that is all I wanted to tell you today.
I think I managed to get all of this in nine minutes. They told me I had 10. So we should have five
minutes for questions. Hopefully you guys have a bunch of questions. But so this is kind of my vision
of the future that we can discretize the human and actually we're trying this with peanut
and sorghum as well. Human and agricultural genomes, we can use those to develop models for
phenotypes that we can actually represent in knowledge graphs to treat people. So treat people faster.
So that's it. And happy to take any questions.
All right. So somebody asked me when will Kuda be released as open source. So there's a full open
source stack and I will say I have no idea. I work in a completely, I work in a very
distant corner of in video where we really focus a lot on genomes. I certainly could ask
whether they're going to tell me I don't know. But one thing I will say is that I work with a lot
with the open source community and there's so many tools developed around Kuda that I think what we
see is open source developers will develop first around Kuda just because it's so much easier
and then moved to other sort of GPU support systems. Yeah.
So I don't know how to answer a question based around low-level stuff, but we can discuss around
beers, I think. But that said, so all of the paratrox modules, we spend a lot of time on
reproducibility and matching to the original algorithms. And so people can check all of that out.
And I mean, for example, we work a lot with the original development team. So for example,
with the variant deep somatic, those sorts of things, to get matching with the GPU processes and
CPU, we also do a lot of work with algorithm developers to try to get similar matching.
And actually optimization as well. Yes.
Yeah. So that's a great question. So somebody is saying, is there
out of stations, server? One thing I would say is, I mean, in genomics, oh yeah, yeah.
I'm stepping back and repeating the question. So if I am remembering the question correctly,
it's, so are we allowing people to go ahead and verify that
special.
Are there signed to add a station artifacts? And I do not know the answer to that question.
So, yep. Anybody else?