Ting Jin

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

The paper identifies genes and regulatory networks driving electrophysiological maturation of neurons in primate prefrontal cortex, with implications for autism.

The paper identifies 46,802 enhancers and 164 psychiatric disorder variants with regulatory effects in the developing cortex and organoids using massively parallel reporter assays and deep learning.

The paper processes single-nuclei multi-omics data from 388 human brains to identify regulatory elements and build cell-type networks, revealing changes in aging and neuropsychiatric disorders.

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

The paper uses single-cell multi-omics data to predict gene regulatory networks in Alzheimer's disease and control, identifying cell-type-specific dysregulation and potential drug targets.

scGRNom predicts cell-type disease genes and regulatory networks from multi-omics data, revealing shared and disease-specific functions in schizophrenia and Alzheimer's Disease.

The paper identifies widespread transcriptomic dysregulation across multiple cortical regions in ASD, showing regional variation and genetic enrichment in synaptic and protein folding genes.

Varmole is a deep learning model that integrates multi-omic networks to prioritize disease risk variants and genes by revealing genomic functions and mechanisms.