Jinze Liu

This study identifies five circulating lipids associated with PTSD severity and shows they can distinguish symptomatic PTSD patients from asymptomatic controls.

This paper presents a single-cell and spatial proteotranscriptomic atlas of human oral tissues, identifying fibroblasts as key immunoregulatory hubs and revealing changes in disease states.

The paper determines sample size requirements for ML classification in bulk RNA-seq data and finds that ML methods generally need larger samples than differential analysis power analysis.

STARComm identifies spatially co-located receptor-ligand interactions in human tissues, revealing disease-specific modules linked to clinical outcomes and drug targets.

The paper identifies CD38⁺ endothelial remodeling as a spatially distinct vasculopathy feature in rapidly advancing oral inflammation using scRNA-seq and spatial proteomics.

This study shows that inhibiting EZH2 in lung cancer enhances immunogenicity and TIL reactivity by altering the tumor microenvironment and T cell properties.

This study analyzes immune cell dynamics in HPV-negative HNSCC using single-cell RNA sequencing to identify molecular signatures and communication patterns associated with disease progression.

This paper introduces PROTAVs, a new vaccine strategy that enhances T cell responses and combination immunotherapy for melanoma by improving antigen processing and cross-presentation.

The paper introduces TACIT, an unsupervised algorithm for cell type annotation in spatial biology, showing improved accuracy and scalability across multiple datasets and applications.

BTR is a deep learning model that recommends bioinformatics tools based on workflow context and tool descriptions, outperforming existing systems.

This study shows that a synthetic zinc finger transcription factor modulates social behaviors by regulating transposable elements and immune response in the prefrontal cortex of mice.

The paper uses nanopore sequencing to improve the zebrafish reference genome assembly, identifying novel structural variants and retrotransposon integration sites.

scSimClassify is a reference-free scRNA-seq cell type classifier using k-mer features, achieving better accuracy than traditional methods in four datasets.

The paper introduces Madi, a workflow combining MALDI-MSI and machine learning to analyze FFPE tissues, revealing metabolic patterns and pathological features in human tissues.

SeqOthello enables efficient querying of large RNA-seq datasets to identify known and novel fusion events with high accuracy and portability.