Publications by authors named "Hong-Wen Deng"

Alzheimer's disease (AD) is a chronic neurodegenerative disorder and the leading cause of dementia, significantly impacting cost, mortality, and burden worldwide. The advent of high-throughput omics technologies, such as genomics, transcriptomics, proteomics, and epigenomics, has revolutionized the molecular understanding of AD. Conventional AI approaches typically require the completion of all omics data at the outset to achieve optimal AD diagnosis, which are inefficient and may be unnecessary.

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  • Short-chain fatty acids (SCFAs) are key metabolites created by gut bacteria from dietary fiber, influencing overall body health but often studied with incomplete data due to research limitations.
  • A new method called MAE (Multi-task Multi-View Attentive Encoders) has been developed to better predict blood SCFA levels by analyzing gut microbiome data alongside dietary and host characteristics.
  • Tests on data from 964 and 171 subjects showed that MAE significantly outperforms older methods in predicting SCFAs and highlights the important roles of gut bacteria, diet, and individual traits in SCFA production.
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Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems.

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Background: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk.

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Elucidating the genetic architecture of DNA methylation is crucial for decoding complex disease etiology. However, current epigenomic studies are often limited by incomplete methylation coverage and heterogeneous tissue samples. Here, we present the first comprehensive, multi-ancestry human methylome atlas of purified human monocytes, generated through integrated whole-genome bisulfite sequencing and whole-genome sequencing from 298 European Americans (EA) and 160 African Americans (AA).

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  • Osteoporosis is a genetic metabolic bone disorder linked to low bone mineral density (BMD), and while single nucleotide variations (SNVs) are known, they don't fully explain BMD heritability, prompting the investigation of larger genomic structural variations (SVs).
  • This study analyzed whole genome sequencing data from 4,982 subjects to identify significant SVs associated with BMD in the hip, femoral neck, and lumbar spine, finding 125, 99, and 83 associations, respectively, which accounted for 13.3% to 19.1% of BMD variation.
  • New genes related to bone health were discovered, including LINC02370 and several ZNF family genes, while the analysis also
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Ferroptosis, characterized by iron‑mediated non‑apoptotic cell death and alterations in lipid redox metabolism, has emerged as a critical process implicated in various cellular functions, including cancer. Aurantio‑obtusin (AO), a bioactive compound derived from Cassiae semen (the dried mature seeds of L. or L.

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Spatial transcriptomics (ST) revolutionizes RNA quantification with high spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. Current methods for predicting spatial gene expression from H&E images often overlook spatial relationships.

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  • DNA methylation is linked to many complex diseases like cancer and autoimmune disorders, but specific key methylation sites are still not well understood.
  • The introduction of MIMOSA, a new resource, enhances the accuracy of DNA methylation predictions by utilizing extensive data from the Genetics of DNA Methylation Consortium, improving upon traditional methods.
  • A case study on high cholesterol using MIMOSA identified 146 potentially causal CpG sites, showcasing its power for discovering disease-related methylation patterns.
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  • - The study addresses the issue of missing data in metabolomics, highlighting how integrating whole-genome sequencing (WGS) can improve data accuracy and completeness in analyses.
  • - A new method using a multi-scale variational autoencoder is proposed to impute unknown metabolites by combining genomic data, including polygenic risk scores and SNPs, with metabolomics information.
  • - The results show that this method outperforms traditional imputation techniques, achieving better data imputation rates, which can enhance the understanding of metabolic pathways and their links to diseases.
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Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations.

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Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems.

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Aging significantly elevates the risk for Alzheimer's disease (AD), contributing to the accumulation of AD pathologies, such as amyloid-β (Aβ), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease.

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Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging.

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Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging.

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Background: The immune microenvironment assumes a significant role in the pathogenesis of osteoarthritis (OA). However, the current biomarkers for the diagnosis and treatment of OA are not satisfactory. Our study aims to identify new OA immune-related biomarkers to direct the prevention and treatment of OA using multi-omics data.

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  • The study investigates functional connectivity (FC) changes in the brain of older adults over two years using fMRI data from the UK Biobank.
  • It finds a significant average increase of 6.8% in connectivity between the somatomotor network and visual network as individuals age, making this connection a strong predictor of the relative age of brain scans.
  • Overall, the research concludes that FC patterns become more pronounced with age, highlighting changes in resting state connectivity in both longitudinal and cross-sectional analyses.
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Unlabelled: Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk.

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  • The study explores the relationship between light, moderate, and vigorous physical activity and dementia risk using data from the UK Biobank involving over 354,000 participants aged 45 and older.
  • The findings reveal a significant mixture effect of physical activities on dementia risk, indicating that vigorous physical activity (VPA) has the most substantial impact on lowering overall dementia risk, while moderate physical activity (MPA) has a greater influence specifically on Alzheimer's disease.
  • The results suggest that engaging in different intensities of physical activity can play a crucial role in reducing the risk of certain types of dementia, with MPA most beneficial for Alzheimer's and VPA for vascular dementia.
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Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding etiology of complex genetic diseases. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost.

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Unlabelled: Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence.

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Background: Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets.

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Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can also reveal the underlying disease mechanisms at the molecular level. In this study, we developed and validated a deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian-cancer patients using multiple independent multi-omics datasets.

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Introduction: The number of primordial follicles (PFs) in mammals determines the ovarian reserve, and impairment of primordial follicle formation (PFF) will cause premature ovarian insufficiency (POI).

Methods: By analyzing public single-cell RNA sequencing performed during PFF on mice and human ovaries, we identified novel functional genes and novel ligand-receptor interaction during PFF. Based on immunofluorescence and in vitro ovarian culture, we confirmed mechanisms of genes and ligand-receptor interaction in PFF.

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Endometriosis is a benign high prevalent disease exhibiting malignant features. However, the underlying pathogenesis and key molecules of endometriosis remain unclear. By integrating and analysis of existing expression profile datasets, we identified coxsackie and adenovirus receptor (CXADR), as a novel key gene in endometriosis.

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