Yue Wang

Stochastic Boolean Model of Death Signaling in MCF7:5C Predicts Cell Death Inducers and Inhibitors

The paper develops a stochastic Boolean model of apoptosis signaling in MCF7:5C cells to predict cell death inducers and inhibitors, identifying genetic and drug-target combinations that affect apoptosis pathways.

MAE-UNETR++: Masked Autoencoder Pretraining for 3-D Lung Nodule Segmentation

This paper proposes MAE-UNETR++ for 3-D lung nodule segmentation, showing that MAE pretraining outperforms random initialization and Decathlon transfer learning in low-data scenarios.

The impact of common variants on gene expression in the human brain: from RNA to protein to schizophrenia risk

The paper identifies eQTLs, rQTLs, and pQTLs in the human brain and shows that many eQTL effects are attenuated post-transcriptionally, yet still correlate with schizophrenia GWAS signals.

Precision and Accuracy in Quantitative Measurement of Gene Expression from Single-Cell/Nuclei RNA Sequencing Data

This study evaluates precision and accuracy in sc/snRNA-seq data, identifies factors affecting reproducibility, and introduces a tool to assess data quality and differential expression reliability.

Prediction of Gene Regulatory Connections with Joint Single-Cell Foundation Models and Graph-Based Learning

This paper introduces scRegNet, a framework combining single-cell foundation models with graph-based learning to predict gene regulatory links, achieving state-of-the-art results on benchmark datasets.

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

swCAM estimates subtype-specific expressions in individual samples using unsupervised deconvolution, revealing changes in cell proportions and gene expression in bipolar disorder.

Cell group analysis reveals changes in upper-layer neurons associated with schizophrenia

The paper uses cell-type deconvolution to analyze gene expression in schizophrenia and finds upper-layer neurons in the DLPFC are associated with SCZ risk.

SynQuant: An Automatic Tool to Quantify Synapses from Microscopy Images

SynQuant quantifies synapses from microscopy images by addressing antibody specificity, brightness heterogeneity, and noise, showing improved performance over existing tools in disease models.

Unsupervised deconvolution of molecular heterogeneity uncovers novel signatures and glia-neuron ratio

The paper investigates the glia-neuron ratio in the brain using unsupervised deconvolution methods on single-cell data from human brains.

Heritability of regional brain volumes in large-scale neuroimaging and genetic studies

The paper examines the heritability of regional brain volumes using genetic and neuroimaging data from multiple cohorts, finding high heritability and consistent patterns across populations.

Mathematical modelling of molecular heterogeneity identifies novel markers and subpopulations in complex tumors

The paper develops an unsupervised method to identify marker genes and subpopulations in tumors without prior knowledge of marker genes or subpopulation composition.