Tag
MIT Technology Review profiles Deanne Taylor, a bioinformatics director who pushed for pediatric representation in the Human Cell Atlas and helped launch the dGTEx project to map healthy gene expression in children, addressing the lack of baseline data for pediatric medicine.
This paper introduces CoCoS, a contrastive pretraining framework that learns whole-cell representations from complementary transcriptomic views, addressing limitations of masked gene reconstruction in single-cell foundation models. Experiments on cell-type annotation and gene regulatory network inference show competitive transfer performance.
This paper proposes JoPMol, a jointly controlled precision molecular generative model that integrates gene expression profiles, molecular structure text, and chemical properties to generate personalized drug candidates, outperforming state-of-the-art methods.
COAST is a context-aware differential learning framework for predicting spatial gene expression from H&E histopathology images, using joint absolute and signed differential regression to capture spatial relationships.
The Hsu and Konermann labs at Arc Institute developed COMBINE, the first platform for systematically testing combinatorial interactions of epigenetic readers, writers, and erasers, screening over 50,000 effector pairs.
This paper presents the first unified benchmark for pathway-guided therapy response modeling, evaluating three biologically informed architectures (BINN, GraphPath, PATH) across five cancer cohorts from The Cancer Genome Atlas for multi-label prediction of targeted therapy, radiation therapy, and survival outcomes.
This paper introduces VCR-Agent, a multi-agent framework that enhances large language models for biological research by generating and validating mechanistic explanations using structured formalism and the VC-TRACES dataset. The approach improves factual precision in gene expression prediction through verified mechanistic reasoning in virtual cells.