Jianwei Shuai

Circadian-Modulated Thresholds as a Mechanistic Basis for Sleep-Wake Transitions, Recovery, and Sleepiness

This paper develops a computational model that explains sleep-wake transitions and sleepiness through circadian-modulated thresholds, showing how circadian and homeostatic drives interact dynamically.

MAIS: an in-vitro sandbox enables adaptive neuromodulation via scalable neural interfaces

This paper introduces MAIS, a scalable neural interface system that enables adaptive neuromodulation through brain organoid-MEA integration and plasticity-driven dynamic decoding.

Fragile polyQ assemblies cause Golgipathy in Huntington's disease

This study identifies polyQ assemblies in HD cells as dynamic structures that regulate Golgi function, showing that mutant HTT disrupts their stability and leads to Golgipathy.

SCTC: inference of developmental potential from single-cell transcriptional complexity

This paper introduces SCTC, a metric for inferring single-cell developmental potential from scRNA-seq data, showing it outperforms gene diversity metrics.

Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data

The paper introduces scPagwas, a method that integrates pathway activation transformation of scRNA-seq data with GWAS summary statistics to identify trait-relevant cell populations and pathways.

Topological design principle for the robustness of necroptosis biphasic, emergent, and coexistent (BEC) dynamics

The paper identifies a core topological circuit for necroptosis BEC dynamics and demonstrates its robustness through analysis of possible circuit topologies.

Dear-DIAXMBD: deep autoencoder for data-independent acquisition proteomics

Dear-DIAXMBD is a deep autoencoder method for analyzing DIA proteomics data, achieving superior performance across different species and instrument platforms.

DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics

DreamDIA-XMBD improves DIA proteomics analysis by using deep representation features for better identification and quantification than existing methods.