Moses Lab

Alan Moses Computational Biology Lab at @UofT

[Moses Lab @AMosesLab]

@random_walker Thanks for bringing this to public attention. It's sad to have to end cold outreach across continents (I did it too in the 00s). But I have been fooled by AI emails. I took on students who couldn't do the research. Pandora's box has been opened. We can't close it now.

[Sandeep Kambhampati @SandeepKambham2]

I'm excited to share our new preprint "Vermeer: Autoregressive generative modeling of microscopy predicts protein localization," a collaboration between @MSFTResearch and @insitubiology!

Preprint: https://t.co/FsEPtzC6pK

Protein localization is fundamental to protein function, but experimental imaging, a key technique to study localization, cannot scale to the entire human proteome across all biological contexts.

To address this challenge, we introduce Vermeer, an autoregressive generative model trained on Human Protein Atlas data @ProteinAtlas. Vermeer synthesizes fluorescence microscopy images of proteins conditioned on cell morphology reference stains and protein sequence embeddings (ESM-C).

Vermeer can leverage latent information about protein localization in the ESM embeddings to generalize to proteins the model has never seen during training. (1/n)

[Andrew Duncan @AgduncanB]

Designing enhancers is key for gene therapy but limited by a lack of experimental data. To overcome this, we developed EnhancAR, a generative model trained on 1.7M enhancer homolog families (~230M sequences) to leverage functional information conserved over evolution.

[Micaela @micaelanonsense]

Announcing our preprint: Predicting evolutionary rate as a pretraining task improves genome language model representations: https://t.co/9ULPccgUyU
Genome language models (gLMs) have the potential to further understanding of regulatory genomics without requiring labeled data...

[Moses Lab @AMosesLab]

@shrinivaslab @RyanKrue @BrennerGroup1 JAX! awesome!

[Taraneh @taraneh_z]

Very happy to share what I've been working on in Barcelona with @BenLehner ! We made many (>400k) mutations in a disordered peptide and the globular domain it interacts with to gain a comprehensive map of how specificity is encoded in a partially fuzzy protein interaction (1/n)

[Kresten Lindorff-Larsen @LindorffLarsen]

Looks interesting:
"A Functional Map of the Human Intrinsically Disordered Proteome" 🍝 by @IPritisanac, @ReidAlderson, @KolaricJess, ..., @JulieFormanKay1, @AMosesLab

https://t.co/Qy7sU3w6VG

[Peter Koo @pkoo562]

Do current genomic language models (pre-trained on whole genomes) learn a foundational understanding of biology in the non-coding region of human genomes?

A new evaluation led by @AmberZqt suggests not yet!

1/N

paper: https://t.co/HUmkG62hdb

[Dmitry Penzar @dmitrypenzar]

Really like the idea from this paper -- https://t.co/iRznpH9Gnc
Also, I like the accuracy of the study, e.g authors compare model trained with their procedure (train on augmented data + finetune on real) not only with baseline model (train on real) but on finetuned baseline model

[Moses Lab @AMosesLab]

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