Quan Hoang Nguyen

This paper identifies 116 risk loci for melanoma through GWAS, links susceptibility to immunity and Wnt signaling, and develops a PRS improving risk prediction.

This study identifies E-selectin as a key driver of IL-1B-dependent neuroinflammation in vincristine-induced neuropathy and suggests targeting E-selectin as a therapeutic strategy.

This paper introduces a framework for creating population-specific genome references using alternate-contig awareness, demonstrating improved variant calling in Vietnamese populations compared to GRCh38.

This study shows that CEP55 promotes tumorigenesis in cancer models and its deletion suppresses tumor growth and improves survival, especially in PTEN-deficient cancers.

SpaMTP integrates spatial metabolomics and transcriptomics data through a comprehensive analysis pipeline and visualization tools.

This study identifies gp130 as a key receptor for a bidirectional neuron-microglia circuit that enhances neuroprotection after CNS injury.

VN1K provides a comprehensive multi-omics and phenomics resource for the Vietnamese population, identifying novel variants and functional associations with disease traits and drug responses.

This study identifies Apolipoprotein E as a key driver of neuroinflammation resolution and recovery from viral encephalitis using a mouse model and patient data.

This study shows that myeloid HDAC7 promotes liver inflammation and glucose dysregulation in obesity, using mouse models and human data.

The paper identifies 354,937 novel lncRNAs across 13 cancer types using single-cell and spatial transcriptomics, revealing their cell-type specificity, spatial distribution, and potential functional roles.

The paper introduces EP-Booster, a method that improves gene expression prediction from H&E images by refining foundation model embeddings with biological knowledge, achieving better performance and interpretability.

This paper describes a tissue multiplexing method to increase spatial transcriptomics output from limited pathology specimens by combining multiple tissues on a single capture array.

This paper integrates multiple foundation models to improve gene prediction in histopathology images, achieving better accuracy for breast cancer subtypes and drug-target genes.

This study develops a spatial pharmaco-multiomics framework to map drug distribution, metabolic niches, and resistance mechanisms in medulloblastoma using integrated spatial and transcriptomic data.

STimage predicts gene expression and classifies cell types from histopathological images, improving robustness and interpretability for cancer and disease analysis.

The paper analyzes spatial immune signatures in HPV-associated cervical tissues to identify immune modulation patterns linked to cancer progression.

The paper integrates 12 spatial and single-cell technologies to analyze cellular interactions and signatures in three skin cancers, identifying key therapeutic targets and genetic associations.

This study identifies Per1 as a systemic regulator of circadian stability, showing that its loss accelerates clock realignment and protects against jetlag-related disruptions.

This study shows that CASP1 self-cleavage terminates its activity in vivo, maintaining homeostasis and preventing inflammasome-driven diseases.

This study uses spatial single-cell analysis to identify 10 cell phenotypes in stage III colorectal cancers and links specific spatial features to recurrence risk.

This study uses spatial transcriptomics to analyze liver fibrosis stages in MASLD, identifying metabolic reprogramming and senescence signatures linked to fibrosis progression.

This study shows that CD8+ T cells prevent reactivation of latent Mtb infection after immunosuppression, while B cells are not required for this protection.

This study uses spatial transcriptomics to analyze a DIPG-infiltrated brainstem, identifying invasion markers and ligand-receptor pairs involved in tumour-TME crosstalk.

The paper develops MMCCI, a computational pipeline to integrate cell-cell interaction results across samples, and applies it to uncover global and condition-specific ligand-receptor interaction patterns in aging brain and melanoma datasets.

The paper identifies and characterizes long non-coding RNAs in dinoflagellates, revealing their functional diversity and role in gene regulation under heat stress.

This paper evaluates deep learning methods for predicting spatial gene expression from histology images in breast cancer, comparing their generalization performance across datasets.

This paper develops a spatial multi-omics platform to analyze immune responses in SARS-CoV-2-infected lung tissues, revealing macrophage infiltration and spatial heterogeneity in cytokine and interferon signaling.

The paper introduces TRIAGE-Cluster and TRIAGE-ParseR to define cell diversity in scRNA-seq data using epigenetic data as a biological anchor.