High-quality datasets that span broad sequence diversity are essential for understanding protein sequence-function relationships beyond local mutational landscapes. Here, we applied Growth-based Quantitative Sequencing (GROQ-seq) to measure function ... read more
Predicting drug-target interactions (DTIs) with deep learning offers opportunities to accelerate drug discovery, yet performance is constrained by the scarcity of target-specific training data. This is a particular challenge for mitochondrial one-car... read more
The NASA Rodent Habitat aboard the International Space Station enabled long-duration studies of behavioral responses to spaceflight, but video-based behavioral analysis has relied on laborious manual annotation. No study has tested whether deep learn... read more
The universe of possible protein sequences is astronomically large, yet our understanding of the sequence-structure relationship is confined to the infinitesimal fraction used currently by life. Determining whether "foldable" architectures are rare s... read more
Early ovarian development establishes the cellular basis of female reproduction, yet its cellular composition and developmental dynamics remain poorly characterized across mammalian species. Here, we generated a cross-species single-cell transcriptom... read more
Disease is a heterogeneous process that involves multiple organs and cell types. Understanding how genomic variation contributes to disease requires approaches that move beyond the linear assumptions of additive models and resolve underlying disease ... read more
Staphylococcus aureus produces a broad range of enterotoxins that act as superantigens, disrupting host immune responses and resulting in a myriad of clinical symptoms. However, large-scale analyses determining enterotoxin gene diversity, lineage str... read more
Despite the breakneck pace of progress in protein design methodology, frontier problems remain challenging, with leading methods struggling to design high-affinity binders, scaffold multiple functional motifs, or stabilize large multi-domain proteins... read more
Fast extraction of physically relevant information from images using deep neural networks has led to significant advances in fluorescence microscopy and its application to the study of biological systems. For example, the application of deep networks... read more
Spatial transcriptomics analysis often involves a myriad of computational methods across diverse platforms, leading analysts to spend excessive time on data assembly rather than deriving biological insights. Current AI solutions tend to either oversi... read more
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