Bernard Ng

This study evaluates deep learning models for gene expression prediction in disease contexts, finding they perform poorly in disease-relevant applications despite high overall correlation.

This study creates a multi-omic atlas of the choroid plexus in Alzheimer's disease, identifying disease-associated cell state transitions and functional impairments.

This study identifies molecular predictors of neuronal dysfunction in Alzheimer's disease by analyzing E/I imbalance and its relationship with neuropathology.

The paper evaluates the AICS-0090 dCas9-KRAB iPSC line for modeling microglia-like cells and their responses to disease-relevant stimuli, finding stimulus-specific transcriptomic and functional responses.

The paper uses spatial transcriptomics to analyze Aβ plaques and glial interactions in AD brains, finding that low Aβ areas show more neuronal loss and high-glia areas show inflammation and neurodegeneration.

The paper investigates structural variants (SVs) in AD/ADRD traits using whole genome sequencing data from two cohort studies, identifying SVs linked to disease phenotypes and suggesting new risk loci.

The paper identifies AD-associated lncRNAs with spatial and cell-type-specific expression patterns and shows OIP5-AS1 contributes to microglial activation in AD.

This study benchmarks deep neural networks for predicting gene expression from DNA sequence across individuals and identifies limitations in predicting variant effects.

This study identifies 928 genes associated with brain weight variation across three datasets and links them to neurodevelopmental processes and brain function.

The paper identifies proteins and molecular processes related to brain connectivity by integrating neuroimaging, proteomic, and gene expression data from human brain regions.

The paper identifies cell-type and cell-subtype specific eQTLs in aging human cortex, revealing variant effects on gene expression and disease associations.

CEWAS improves gene detection from GWAS by modeling cascading epigenomic and transcriptomic effects, identifying disease-related genes and novel biological processes.

This study identifies a genetic variant in CSMD1 associated with amygdala connectivity and prosocial behavior through a GWAS of brain networks in adolescents.

The paper evaluates methods for estimating cell-type proportions in brain tissue using paired transcriptomic and immunohistochemistry data, showing some algorithms provide accurate estimates.

The paper identifies novel modifier genes for Huntington disease by combining population-scale gene expression data with patient genomic data.

This study identifies traits associated with polygenic risk for depression using UK Biobank data and finds causal links between depression risk and brain structure, as well as environmental interactions.

This paper introduces AI-TAC, a convolutional neural network that infers cell-type-specific chromatin activity from DNA sequences using an ATAC-seq atlas of immune cells.

The paper applies polygenic risk scoring to a large family with bipolar and depressive disorders to study genetic risk patterns and anticipation.

This study identifies splicing variations in aging brains linked to Alzheimer’s Disease, finds genetic regulation of splicing, and highlights shared pathways with other neurodegenerative diseases.

This paper estimates DNA methylation networks using a sparse latent Gaussian graphical model and shows it explains gene expression patterns in unseen datasets.

The paper identifies xQTLs in the human brain integrating RNA, DNA methylation, and histone acetylation data from 411 individuals, revealing 18 new CNS disease susceptibility loci.