Unlocking Cells' Secrets: Diffusion, Deconvolution, & Discovery with Siyu He, author of Squidiff & CORAL
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What is the overall focus of this episode and who is the guest?
Hello, and welcome back to the Cognitive Revolution. Today I'm excited to share an in-depth conversation with Si Yu Hu, postdoc at Stanford in Biomedical Data Science, and lead author of two notable recent papers that use intricate machine learning systems to shed light on two of the great questions in biology. What causes what at the cellular level? And how do the interactions between individual cells give rise to higher level tissue development and disease? The first paper, Squiddiff, allows us to predict how cells will respond to perturbations by conditioning a diffusion model trained to predict transcriptome states, or the level of gene expression that a cell exhibits across more than thirty thousand individual genes, on semantic directions derived from other experiments.
This approach, based on a very modest amount of data, can save researchers months that would otherwise be needed to culture specific cell types, and can also shed light on intermediate states that can be extremely challenging to measure directly. The architecture will be familiar to students of image generation models, and also reminds me quite a bit of the mind's eye models used to reconstruct images from brain scan data that we've covered on previous episodes. This is another data point showing just how general purpose today's architectures really are, how models with quote unquote intuitive understanding of important problem spaces like transcriptomics can perform many orders of magnitude faster than physics based simulations, and ultimately how much future discoveries in biology will be made initially in silico and then confirmed by much slower and more costly wet lab ground truth experiments.
How do transcriptomes work and why was Squidiff created?
The second paper, Coral, makes it possible to combine low resolution tissue data and high resolution single cell profiles into a single integrated view. This is a particularly intricate system that uses graph neural networks and ultimately deconvolves the lower resolution tissue data into a detailed cell by cell picture, thus enabling closer and more insightful study of the tissue as a whole. This project makes notable use of synthetic data and reminds me of the project we covered a few months back from Michael Levin and his co authors, which had connected gene, drug, and disease information to make previously unknown connections. Perhaps most telling, CUN team completed both of these projects under the supervision of Professor James Xiao, who we recently had on the show to talk about the virtual lab project, which used a team of AI agents equipped with specialist biology models to develop new nanobodies capable of treating emerging COVID variants, and also the Protein Language Model Interpretability Project Inter PLM.
That all of this work could come from one group in such a short period of time supports the broad theme that everything is working in AI. And with just a bit more acceleration, perhaps driven by the inevitable generalizations and scale ups of the techniques we discuss today, we really could see a century's worth of biology progress in just the next decade. As always, if you're finding value in the show, we'd appreciate it if you'd share it with friends, post about it online, or leave a review on your favorite podcast platform. Of course, we always invite your feedback and suggestions too. You can reach us via our website, cognitive revolution.ai, and you're always welcome to DM me on your favorite social network.
Now, I hope you enjoy this deep dive into cutting edge architectures that are advancing the frontiers of cell and tissue biology. With Stanford researcher Si Yu Hu. Lead author of Squiddiff and Coral. See you huh, postdoc at Stanford in biomedical data science and lead author of the recent AI for biology papers Squiddiff and Coral. Welcome to the Cognitive Revolution.
Thank you, Nelson. It's good to be here and very excited to be here to share my research to a broader audience.
Cool.
What data and model architecture does Squidiff use?
Well, uh we've got a lot to unpack, so let's maybe just start with a little bit of big picture context setting.
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Chapters
8 chapters
1
What is the overall focus of this episode and who is the guest?
0:00–1:42
2
How do transcriptomes work and why was Squidiff created?
1:42–3:59
3
What data and model architecture does Squidiff use?
3:59–5:02
4
Why are diffusion models useful for biological simulation?
5:02–6:37
5
How are in‑silico experiments validated against real data?
6:37–8:11
6
How can AI models accelerate biomedical research and what are the next steps?
8:11–9:18
7
What is the CORAL project and how does it integrate spatial transcriptomics and proteomics?
9:18–12:52
8
What role does synthetic data play in model validation and what is the future of AI in healthcare?
12:52–1:39:56
Speakers
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