Jiahuai Han

This study identifies acetate as a critical metabolic fuel for the heart during fasting, showing its uptake and utilization via Acss1-dependent pathway.

The paper identifies the Z-RNA-ZBP1 axis as a driver of Tau-mediated neurodegeneration and shows that ZBP1 inhibition can alleviate cognitive deficits in Tau-transgenic mice.

SUREv2 compresses single-cell atlases while preserving distribution, enabling privacy-protected data sharing and novel cell type discovery.

SURE establishes a standardized cell coordinate system for cell identification using metacells, enabling atlas functionalities and integration of large-scale datasets.

CleverXMBD1 improves cell abundance quantification by using a confidence-based multi-positive and unlabeled loss function, outperforming existing methods across multiple datasets.

The paper identifies that pathogenic NLR mutations cause PELO-independent ATPase activation, leading to inflammatory responses and disease, and suggests this as a diagnostic marker.

This paper introduces scPhenoXMBD, a deep auto-regressive factor model that extracts biological signals from single-cell transcriptomics data, improving analysis reliability and enabling better identification of IFNβ-responsive pathways.

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

scPheno integrates scRNA-seq with disease phenotypes to identify transcriptional variations linked to COVID-19 diagnosis and severity, achieving 81.3% accuracy using 4 genes.

DAISM-DNNXMBD improves cell type proportion estimation in bulk RNA-seq by using in silico data augmentation and deep neural networks, outperforming existing methods on real-world datasets.

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

Dice-XMBD improves cell segmentation accuracy for imaging mass cytometry data compared to existing methods.