Jiebiao Wang

The paper introduces SPInfer, a workflow for spatial proteomic inference that improves cell segmentation in human brain tissue, enabling better biological discovery at population scale.

The paper introduces a supervised learning algorithm for multi-phenotype genomic prediction, showing improved performance in gene expression and DNA methylation analysis and cell type deconvolution.

BLEND improves cellular deconvolution by selecting optimal references and avoiding data preprocessing, showing better performance on real and simulated data.

HiDecon estimates rare cell type fractions in bulk transcriptomics data using hierarchical deconvolution with single-cell references and cell type trees.

DeepGAMI improves phenotype prediction from multimodal data by integrating biological knowledge and imputing missing modalities, outperforming existing methods and providing interpretable insights.

scMD estimates cell type fractions from bulk DNAm data using single-cell DNA methylation references, showing improved performance in identifying Alzheimer's disease-related methylation patterns.

The paper examines RNA editing changes during human cortical development, identifying expansion of editing sites and their genetic regulation linked to neurological disorders.

The paper identifies population-level variation in enhancer expression in the human brain and links enhancer eQTL to schizophrenia heritability.

This study integrates spatial transcriptome, single-cell RNA-seq, and single-cell ATAC-seq data from mouse lungs after immunization to identify cell types and their spatial dynamics.

The paper develops a Bayesian method to estimate cell-type-specific gene expression from bulk RNA-seq data using priors from scRNA-seq, improving accuracy and enabling downstream analyses like eQTL discovery.

This study identifies transcriptional changes in opioid use disorder brains, linking neuroinflammation and synaptic remodeling through RNA-seq and integrative analyses.

The paper identifies cell-type-specific marker genes from bulk transcriptome data by refining published markers using correlation patterns.

The paper identifies differentially expressed transcripts in OCD patients' striatum and OFC, showing enrichment for synapse-associated genes and changes in cell type fractions.

ESCO simulates single-cell gene expression with co-expression networks, showing imputation improves GCN recovery except when data are too sparse.

This paper performs a large-scale eQTL meta-analysis across cerebral cortical and cerebellar brain regions, identifying distinct eQTL patterns and demonstrating improved power over existing studies.

The paper functionally annotates rare structural variants in the human brain by integrating SV calling with RNA-seq data to assess their regulatory effects and prioritize pathogenic deletions.

The paper introduces CCmed methods to detect robust trans-eQTLs and assess their effects on human traits by analyzing brain tissue data from GTEx and validating findings with schizophrenia GWAS data.

The paper estimates subject- and cell-type-specific gene expression from tissue using an empirical Bayes method, enabling downstream analyses like eQTL identification.

This study identifies 99 genes associated with autism spectrum disorder through large-scale exome sequencing, showing differences in mutation frequencies between ASD and severe neurodevelopmental delay groups.

This paper introduces GMAC, a method to identify cis-mediators of trans-eQTLs across tissues using GTEx data, improving mediation analysis by adaptively adjusting for confounders.