H. Goodarzi

PIE: Generalizing perturbation effects across unseen perturbations, contexts and datasets

Predicting cellular response to perturbations is key to understanding biological mechanisms and selecting therapeutic targets. However, generalizing across cellular contexts, perturbations, and experimental datasets remains challenging due to incomplete representations of the system being perturbed and the difficulty o...

A massively parallel synthetic gene atlas for learning compact cis-regulatory grammar across cellular contexts

Virtual-cell models increasingly learn from large perturbation atlases, but their view of cis regulation remains limited to endogenous genes embedded in broad native regulatory contexts. Here we introduce Therna Biosciences’ Chronos platform and its first public dataset release, comprising two complementary modules of...

Extracellular cGAMP transmission exacerbates neuroinflammation in aging and multiple sclerosis

Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.

Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions, advances the ability to model gene regulation, drug response, and network dynamics.

Predicting cellular responses to perturbation across diverse contexts with State.

The performance and flexibility of State set the stage for scaling the development of AI models of cell state, a machine learning model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments.

scBaseCount: An AI agent-curated, standardized, auto-updated single-cell data repository.

Single-cell RNA sequencing has transformed cell biology by enabling precise transcriptomic measurements of individual cells. The Sequence Read Archive (SRA) is the largest public repository of sequencing reads, yet much of it remains underutilized due to unstandardized metadata. Here, we introduce scBaseCount, a databa...