Ziheng Chen

The paper introduces CTACIT to fine-map neuropsychiatric regulatory variants by integrating conservation scores with cell type-specific open chromatin data across species.

The paper identifies context-dependent regulatory variants in Alzheimer's disease by analyzing their effects across different cellular contexts using MPRAs and machine learning.

The paper develops a platform combining machine learning and multiplexed in situ screening to identify enhancers with cell-type and behavioral specificity for neural circuit engineering.

The paper introduces WhAMPRA, an in vivo MPRA platform in mice, to assess tissue-specific regulatory functions of genetic variants and enhancers.

The paper links chronic pain genetic variants to dorsal horn neuron subtypes using multi-species single-cell and spatial transcriptomic analyses.

The paper uses CNN regression models to predict the impact of AD-associated variants on open chromatin in different cell types, finding greater effects in peripheral cells than in microglia or neurons.

The paper identifies a fail-safe mechanism for surveilling translation in noncoding regions, showing that hydrophobic C-terminal tails of noncoding-derived polypeptides are targeted by the BAG6 complex for degradation or membrane localization.

This paper demonstrates that targeting a single marker RNA with CRISPR/RfxCas13d causes collateral transcriptome destruction, enabling inhibition of cell proliferation or suppression of cell state transition in human cells.