Ran Zhang

This study determines the structural basis for differential inhibition of THIK1 by chemically related inhibitors using cryo-EM, showing how they affect different gates despite sharing a common binding site.

Cavebear improves pseudotime inference by using reference species time-series data to guide ordering of cells in query species or conditions without time labels.

The paper introduces Icebear, a neural network framework for cross-species imputation and comparison of single-cell transcriptomic profiles, revealing evolutionary adaptations of X-chromosome upregulation in mammals.

This study identifies astrocyte-derived extracellular matrix proteins, particularly SPARC, as regulators of synapse remodeling in stress-induced depression.

This paper introduces Sunbear, a framework for integrating multi-condition and multi-modal single-cell profiles across time to infer temporal profiles and predict dynamic relationships in mouse embryonic development.

The paper introduces Sceptic, a supervised pseudotime analysis model that improves prediction accuracy over existing methods for scRNA-seq and single-nucleus image data.

LSMMD-MA scales multimodal data integration for single-cell genomics to handle up to a million cells per modality, improving on existing methods by two orders of magnitude.

The paper introduces MultiomicsTracks96, a high-throughput multi-omics workflow for analyzing frozen and FFPE tissues, showing high correlation between data types and improved protein prediction using multi-omics data.

The paper identifies region-specific astrocyte genes and pathways linked to Alzheimer's disease susceptibility using a network-guided approach and mouse gene expression data.

Polarbear translates between single-cell modalities using semi-supervised learning, improving accuracy over existing methods and enabling cross-modality cell matching.

This study uses a machine-learning approach to predict autism-associated genes and identifies key pathways and developmental stages involved in ASD.