Chris Gaiteri

The paper identifies robust multicellular axes associated with Alzheimer's disease pathology and sex using single-cell data from 427 donors, validated in an independent dataset.

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.

The paper develops a multi-omics cohort of prefrontal cortex data from cognitively normal older adults to identify molecular signatures of cognitive resilience to Alzheimer's disease.

The paper introduces an adversarial neural network to identify genetic resilience markers in Alzheimer's disease, finding five SNPs associated with resilience independent of polygenic risk scores.

This paper develops a multi-stage graph attention network to improve Alzheimer's disease classification from genome-wide association data by capturing epistatic interactions and enhancing interpretability.

This study integrates multi-omics data from aged human brains to identify cross-system biological factors and molecular subtypes associated with Alzheimer's disease heterogeneity.

The paper develops a model to predict gene relevance in behavioral research using a large language model, revealing shared gene signatures between molecular and behavioral studies.

The paper identifies cell type-specific gene modules associated with Alzheimer's Disease traits using snRNASeq data from human brain tissues.

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 identifies YTHDF proteins as key regulators of Alzheimer's disease brain gene signatures through m6A-dependent and independent mechanisms.

The paper identifies an AD-like subtype in brain aging across multiple regions, showing overlap with AD gene signatures and regional differences in gene expression.

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

This study provides the first single-nuclei profiling of human leptomeninges, identifying cell types and gene expression patterns linked to Alzheimer's disease.

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 develops PCBrainAge, an epigenetic age predictor for brain tissue, showing it better captures aging heterogeneity and links to Alzheimer's disease features than existing methods.

The paper analyzes proteomic data from Alzheimer's disease brains, revealing disease-related changes not seen at the RNA level and identifying potential therapeutic targets.

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

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

This paper introduces DEcode, a deep learning model that improves differential gene expression prediction by integrating systems biology data, showing post-translational RNA-binding factors are key regulators.

Coexpression meta-analysis of 2114 human brain RNA-seq samples across the AMP-AD consortium identifies five consensus Alzheimer's disease transcriptional clusters, including two sex-heterogeneous clusters and a previously unreported proteostasis cluster.

Molecular network analysis of RNA sequencing data from the aging human cortex identifies new Alzheimer's and cognitive decline genes.

The paper identifies gene modules in human frontal cortex microglia associated with beta-amyloid and tau pathology in Alzheimer's disease.

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.