My message to Trump’s NIH Director? No one in America wants us to do LESS cancer research.
No one is asking Trump to make it harder to cure Alzheimer's disease.
Yet Trump is cutting all of this NOW and demanding an $18 BILLION cut to NIH next year. Not on my watch.
According to a 2021 report, the University of California system:
• generated $82B in economic activity in California
• supported 529K jobs in the state
• generated $21 in economic output for every $1 received
Public divestment from higher ed makes no sense, even in the narrowest economic terms.
I'm excited to release what I've been cooking up the past few months at @arcinstitute.org
BINSEQ is a family of binary file formats for sequencing data built with paired records and parallel processing in mind with big performance gains (2x-40x) over gzip-fastq with similar storage
I want to thank all my co-authors from UCSF, the Laboratory for Genomics Research, and GSK for making this project a reality. I especially want to express my deepest appreciation for @kampmann.bsky.social , Adam Yala, and Michael Keiser for being such incredible mentors!
Please dive into the preprint to learn more! We hope this work inspires the further development of representation learning methods for dynamic biological phenotypes and that Plexus can be a useful tool for gaining deeper insights from high-content screens. 8/
Projecting the gene knockdown-induced phenotypes onto this axis pointed toward the role of dysregulated excitatory synaptic activity in driving the aberrant activity phenotype. 7/
Finally, we incorporated an iPSC-derived isogenic cell-line model of mutant MAPT to study a disease-relevant aberrant activity phenotype. Leveraging the learned embeddings, we generated a “disease axis” that best linearly explains the differences between wild-type and mutant MAPT phenotypes. 6/
We then showed that we can map relevant signal processing features onto the learned features to enable physiological interpretability, using the knockdown of the gene KCNQ2 as an example. 5/
We then developed a CRISPRi-enabled iPSC-derived astrocyte and neuron co-culture model to study the effect of 52 genetic perturbations on neuronal activity dynamics. Using the learned embeddings, we recovered ~1.5x as many generalizable phenotypes compared to manual feature engineering. 4/
We validate that these learned embeddings outperform signal processing-based manual features and traditional masked autoencoders when used for the linear classification of distinct simulated and experimental neuronal activity phenotypes. 3/
This image contains a diagram explaining the architecture of the self-supervised learning model and the model i
In this project, we developed a self-supervised model we call Plexus, which uses a novel single-cell encoding strategy to efficiently learn patterns of both intrinsic excitibility and network-level neuronal activity measured via calcium imaging. 2/
