Saniya Khullar

Single-nucleus transcriptome-wide association study of human brain disorders

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.

Single-Nucleus Atlas of Cell-Type Specific Genetic Regulation in the Human Brain

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

An Integrated Single-Nucleus Atlas Resolves Cell-Type-Specific Programs and Molecular Subtypes in Alzheimer's Disease

The paper constructs a single-nucleus transcriptomic atlas of Alzheimer's disease and identifies cell-type-specific molecular subtypes and therapeutic targets using a new framework.

Integration of Nuclear Receptors into a Schwann cell Gene Regulatory Network

This study identifies a NR2F-regulated gene network in Schwann cells, revealing enrichment for nonmyelinating Schwann cell genes and novel transcription factor interactions.

Cell-type-resolved genetic regulatory variation shapes inflammatory bowel disease risk

The paper maps cell-type-specific eQTLs in IBD patients to identify effector genes and mechanisms linking genetic risk to disease.

TabVI: Leveraging Lightweight Transformer Architectures to Learn Biologically Meaningful Cellular Representations

TabVI improves cell type annotation and integration using a probabilistic transformer model, producing interpretable feature attention masks and robust performance across dataset sizes.

NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation

NetREm identifies cell-type-specific transcription factor coordination for gene regulation using single-cell data and validates findings with functional genomics.

Deaf1 regulates the coordinated repression of synaptic genes to limit synaptogenesis

The paper identifies DEAF1 as a repressor of synaptic genes, showing its role in limiting synaptogenesis and linking it to intellectual disability.

Massively parallel characterization of psychiatric disorder-associated and cell-type-specific regulatory elements in the developing human cortex

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.

Single-cell genomics and regulatory networks for 388 human brains

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: Deep biologically guided auxiliary learning for multimodal integration and imputation to improve phenotype prediction

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

Age-maintained human neurons demonstrate a developmental loss of intrinsic neurite growth ability

Directly-reprogrammed human neurons show reduced neurite growth ability with age, mirroring rodent developmental patterns.

Illuminating links between cis-regulators and trans-acting variants in the human prefrontal cortex

The paper identifies trans-eQTLs in the human prefrontal cortex and links them to schizophrenia risk genes, showing their role in disease mechanisms.

Single-cell network biology characterizes cell-type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

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.

Brain and Organoid Manifold Alignment (BOMA), a machine learning framework for comparative gene expression analysis across brains and organoids

BOMA identifies conserved and specific developmental gene expression programs between human brains and organoids at cellular resolution.

Predicting gene regulatory networks from multi-omics to link genetic risk variants and neuroimmunology to Alzheimers disease phenotypes

The paper predicts gene regulatory networks from multi-omics data to link genetic risk variants with neuroimmunology and Alzheimer's disease phenotypes, identifying potential biomarkers.