My thesis work on active machine learning to model regulatory DNA is now out in Cell Systems!

We answer the question: When you can synthesize any DNA sequence you want, how do you decide which ones are worth testing?

www.sciencedirect.com/science/arti...

We analyzed a second pair of sequences with similar motif content. The model correctly predicts that the RORB motif must be 3' of the CRX motif.

These results show our model learns the context that distinguishes functionally non-equivalent motifs.

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RORB motifs have a wide range of effects when mutated. Our model predicts this correctly & these effects are correlated with motif affinity.

Along with our other results, this shows active learning generates the data needed to learn regulatory grammars.

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We have a new result showing that our model accurately predicts when CRX motifs increase vs. decrease expression. This is crucial because nc variants can change activity in unexpected directions, so it's important to have data that can tell when a motif has a positive vs negative effect.

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When we did many rounds, active learning was more efficient, approached the upper bound with less data, and enriched for positive examples!

This demonstrates that active learning is broadly effective and illustrate that enriching for active sequences is more informative

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We tested active learning in a second system using Nadav Ahituv and @jshendure.bsky.social's genome-wide MPRA in K562s. We downsampled the data, trained a CNN, then sampled from the remaining data. Active learning consistently outperformed random sampling across many starting conditions.

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