Xiaobo Zhou

This paper introduces SPAE, a model for analyzing cell cycle dynamics and states in scRNA-seq data, showing improved accuracy and robustness in cell cycle characterization and cancer cell cycle transition prediction.

Spatium is a foundation model for spatial proteomics that learns stable cell representations from constrained protein measurements, enabling accurate cell identity recovery and spatial microenvironment analysis.

The paper introduces CCAN, a model that removes cell cycle effects from integrated single-cell multi-omics data while preserving cell type-specific variations, showing strong performance in downstream tasks.

This study develops a 2YS logistic regression model using radiogenomics analysis to predict GBM patient survival based on MRI features and gene associations.

HGNNPIP improves PPI prediction by addressing data bias through a hybrid graph neural network framework, outperforming existing methods on benchmark datasets.

The paper identifies 14 metastasis-related mutated genes and develops a Mscore to predict metastatic risk in primary prostate tumors using xgboost.

The paper identifies 25 RNA editing biomarkers in Parkinson's disease by analyzing their potential impact on miRNA regulations of 133 PD-related genes.

The paper identifies 24,236 potentially functional A-to-I RNA editing events across 33 cancer types from The Cancer Genome Atlas, linking them to tumorigenesis through altered expression, protein function, and miRNA regulation.

The paper identifies genetic variants influencing A-to-I RNA editing in neurodegenerative diseases using large-scale transcriptomic and genetic data from multiple brain tissues.

The paper identifies APA events associated with TNBC prognosis, highlighting a novel biomarker in UBASH3B that affects microRNA regulation.

CCPE estimates cell cycle pseudotime and identifies cell cycle phases from scRNA-seq data, showing effectiveness and robustness in simulated and real datasets.

This study develops a 3D multi-scale model of AD to understand microglia's role in disease progression and predict therapeutic strategies.

This study introduces ESOCVD, a computational platform to detect circadian gene expression patterns in AD using untimed transcriptome data, revealing five circadian alteration patterns in AD patients.

scIGANs uses GANs to impute dropouts in scRNA-seq data, improving downstream analysis and performance across datasets.

CaSpER identifies and analyzes CNV events in single-cell and bulk RNA sequencing data, revealing novel sub-clones and therapeutic targets in GBM.

The paper analyzes miRNA-gene positive correlations across 31 cancers using TCGA data, finding their prevalence, clinical relevance, and potential epigenetic mechanisms.

The paper identifies histone modifications associated with alternative splicing events in human ESCs and shows their role in cell cycle regulation and fate decision.

The paper introduces DeepCode, a deep learning model that integrates epigenetic features to predict splicing patterns during hESC differentiation and identifies a novel mechanism linking histone modifications to cell fate decisions.