Jin Chen

This paper develops and applies TR-LSCI for depth-sensitive, high-speed CBF imaging during ICP perturbations, revealing phase-dependent autoregulation transitions.

This study identifies P2x2 receptor upregulation in the cochlea as a cause of hyperacusis, showing that antagonists can reduce hearing hypersensitivity.

This paper introduces DPLM, a dynamics-aware protein language model that integrates molecular dynamics simulations with sequence data to improve predictions of protein properties.

This study shows that Cx26 deficiency in the cochlea leads to increased Cx46 expression, which compensates by forming gap junctions in the same locations as Cx26.

S-PLM improves protein language models by integrating 3D structure information through contrastive learning, outperforming sequence-only models in clustering and classification tasks.

This study identifies the ATP-gated P2x7 receptor as a key channel in type II auditory nerves, showing its role in hearing sensitivity and noise protection through efferent control mechanisms.

The paper evaluates AlphaFold2's fairness in predicting protein 3D structures by analyzing PLDDT score variability across amino acid types, secondary structures, and sequence lengths, revealing systematic biases.

This study shows that Cx26 heterozygous mutations increase hearing sensitivity and noise susceptibility, challenging the previous view of their harmlessness.

SSRE improves cell type identification in scRNA-seq by enhancing similarity measurement through sparse subspace representation and gene selection.

This study shows that MOC efferent nerves also innervate cochlear supporting cells, modulating gap junctions and affecting OHC electromotility, which impacts hearing sensitivity and noise protection.

xQTLImp imputes missing xQTL summary statistics using multivariate Gaussian approximation, rediscovering novel significant eQTL associations in a single-cell dataset.

This paper introduces TEP-Finder, an algorithm to identify emerging phenomena in plant phenotyping data, showing it finds more functionally specific and biologically significant patterns than existing methods.

GANai improves CT image standardization by addressing radiomic feature discrepancies through a novel GAN model with an alternative training strategy.

OLIVER is a tool for visual data analysis of longitudinal plant phenomics data, enabling real-time updates and hypothesis generation through integrated visualization and statistical testing.

This paper introduces FSM, an algorithm for network motif discovery that improves efficiency and shows that large, frequent motifs are better for inferring higher-order biological network structures.