Nicholas Ho

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@eigenNick

AI for Biology PhD Student @GriffinCMU | Advised by @jmuiuc and @ericxing | 🪀

nickdst.github.io Joined June 2021

730 Following 239 Followers 144 Posts

Latest posts 5

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Nicholas Ho @eigenNick 5d

Contextualized gene representations work surprisingly well for predicting multigene perturbation effects.

I’m excited to introduce EMBER, a pretrained gene-centric foundation model designed specifically to learn context-specific gene representations. A joint work with @jmuiuc and @ericxing.

With innovations in single-cell pretraining and dataset curation, EMBER gene representations can be combined with a lightweight probe for single gene knockouts, genetic interactions (double knockouts), and even drug perturbations.

Both a 🧵 and a 🎥, got in front of a camera to share some highlights! (1/n)

4:12

Nicholas Ho @eigenNick 4d

This is fantastic! I think one unfortunate side effect of ML research's emphasis on architectural novelty is that adding more bells and whistles can make it hard to tell where the performance gains are actually coming from. Ablations help, but there are always more comparisons to run haha.

Strong simple baselines help keep things grounded and put the results in context.

Congrats @probablybots and team!

Caleb Ellington @probablybots 4d

We used coding agents to automatically build baselines for 36 "AI for science" deep learning methods from Nature, Science, and Cell-family publications. These baselines recovered a median of ~95% of the score of the published method, and beat them in 33% of cases.

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Nicholas Ho @eigenNick Sep 15

I very like! this is a good reminder to always be familiar with the original data generation process

Euxhen Hasanaj @EuxhenH Sep 14

If every step in a scRNA-seq experiment loses just 1% of the signal, you lose almost 40% of the total signal after 50 steps.

Noise and poor reproducibility are common problems when working with this kind of data. Yet, as computational biologists, we often treat data collection as a black box and start our work from the count matrix.

I wrote a post about what can go wrong before that matrix reaches us, from sample selection and tissue handling all the way to sequencing and read counting. I hope it helps other computational biologists better understand where their data came from.

Post: euxhenh.com/blog/scrnaseq-…
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Nicholas Ho @eigenNick Sep 2

I really like this analogy

Jian Ma Verified account @jmuiuc Sep 1

@yuejiec very true. just like cooking - AI gives us all a better equipped kitchen but what matters is the chef's taste, what / how they actually serve, and whether the meal is memorable 🍳
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Nicholas Ho @eigenNick Apr 10, 2025

Amazing work! Congrats to @ellie_haber and @SpencerKrieger

Jian Ma Verified account @jmuiuc Apr 10, 2025

Integrating spatial transcriptomics across platforms is hard - different gene panels, sparse data. We introduce LLOKI, using optimal transport + single-cell FMs for unified ST integration. Work led by @ellie_haber (@mldcmu) & Lane Fellow @SpencerKrieger biorxiv.org/content/10.110…
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