ML x Bio @ Prima Mente
Joined March 2018
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Yash Shirsath retweeted
Introducing In-Context Module discovery (ICM): interpretable gene modules for comparing cell states across datasets. ICM is the latest advance for our virtual cell modelling framework: Parthenon.
ICM learns from primary human cells across 66 datasets and adapts its modules to new contexts without retraining.
In microglia, ICM recovers known state signatures, reveals unexpected cellular features in experimental models, and helps explain why a specific perturbation strongly shifts gene expression in one model and barely in another.
This gives biologists better tools choose perturbations and design better in vitro experiments.
In 2009, ImageNet helped kick off the modern deep learning revolution by open sourcing 3.2M captioned images.
Today, we have ~600M+ public single-cell transcriptomes. Nearly 200x as many observations.
Yet, the field has still struggled to identify molecular drivers of diseases like Alzheimer's.
Everyone talks about AI for science, but we need the right causal data in the right disease-relevant contexts to REALLY harness the power of deep learning in bio.
Incredibly grateful to be part of the team bringing forward this massive new 150M cell dataset of neurons, astrocytes, and microglia.
More to come.
Today we're launching the Alzheimer's Translation Challenge, a global AI competition to discover new therapeutic hypotheses for Alzheimer's disease.
At its core is a new 150M cell atlas of neurons, astrocytes, and microglia across different genetic backgrounds under combinatorial perturbations, with multi-modal readouts.
The data will be made available through the AD workbench, Hugging Face and Prima Mente's modeling platform.
The challenge is led by @PrimaMente and @AlzData in collaboration with @nvidia, @huggingface, @nebiusai, Talisman Therapeutics, @UltimaGenomics, @cellanome, @PrimeIntellect, @GoodfireAI, and @boltz_bio.
In 1906, Alois Alzheimer described a new disease of the brain. After 120 years of progress, we still don't fully understand what drives this disease. We want to change that.
Participants will use the dataset to build AI models, assessed through a series of evals from general benchmarks to more complex tasks.
Top teams will be shortlisted, and their experimental hypotheses tested in Prima Mente's wet lab. Teams win when their hypotheses demonstrate the predicted molecular and functional effect.
Yash Shirsath retweeted
Today we're launching the Alzheimer's Translation Challenge, a global AI competition to discover new therapeutic hypotheses for Alzheimer's disease.
At its core is a new 150M cell atlas of neurons, astrocytes, and microglia across different genetic backgrounds under combinatorial perturbations, with multi-modal readouts.
The data will be made available through the AD workbench, Hugging Face and Prima Mente's modeling platform.
The challenge is led by @PrimaMente and @AlzData in collaboration with @nvidia, @huggingface, @nebiusai, Talisman Therapeutics, @UltimaGenomics, @cellanome, @PrimeIntellect, @GoodfireAI, and @boltz_bio.
In 1906, Alois Alzheimer described a new disease of the brain. After 120 years of progress, we still don't fully understand what drives this disease. We want to change that.
Participants will use the dataset to build AI models, assessed through a series of evals from general benchmarks to more complex tasks.
Top teams will be shortlisted, and their experimental hypotheses tested in Prima Mente's wet lab. Teams win when their hypotheses demonstrate the predicted molecular and functional effect.