Biomedical subjects

Yifan Yang

Publications and source records attributed to Yifan Yang.

5 recordsLinked to original sources

PubMed2026

Knowledge-guided contextual gene set analysis with large language models.

MOTIVATION: Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conventional GSA methods overlook clinical context of the analyses, often generating long lists of enriched pathways with redundant, nonspecific, or irrelevant results. Interpreting these requires extensive, ad-hoc manual effort, reducing both reliability and reproducibility. RESULTS: We introduce cGSA, a novel AI-driven framework that enhances GSA by incorporating context-aware pathway prioritization. cGSA integrates gene cluster detection, enrichment analysis, and large language models to identify pathways that are not only statistically significant but also biologically meaningful. Benchmarking on 102 curated gene sets across 19 diseases and ten disease-related biological mechanisms shows that cGSA outperforms baseline methods by over 30%, with expert validation confirming its increased precision and interpretability. Two independent case studies in melanoma and breast cancer further demonstrate its potential to uncover context-specific insights and support targeted hypothesis. AVAILABILITY AND IMPLEMENTATION: The demo website is publicly available at https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/cGSA/, while the data and code can be accessed at https://github.com/ncbi-nlp/cGSA.

Large Language Models↗

PubMed2026

MdWRKY75 interacts with MdWOX11 to modulate root growth under salt stress in apple.

The root system is pivotal for plant development, enabling both vegetative growth and tolerance to abiotic stresses like salinity. However, the molecular mechanisms governing root adaptive development in response to salt stress remain poorly understood in apple (Malus domestica Borkh.). In this study, we identified the salt stress-responsive WRKY transcription factor MdWRKY75. Overexpression of MdWRKY75 in transgenic apple negatively regulates adventitious root (AR) formation and salt stress tolerance, whereas reducing MdWRKY75 expression yields the opposite phenotype. Moreover, MdWRKY75 directly binds to the promoter of MdSAUR15 (SMALL AUXIN UP RNA15) and transcriptionally represses the expression of MdSAUR15, which, when overexpressed, promotes AR formation and enhances salt stress tolerance. We further demonstrated that MdWRKY75 interacts with MdWOX11, a WUSCHEL-related homeobox (WOX) transcription factor, both in vitro and in vivo. MdWOX11 expression is upregulated and enhances AR formation under salt stress. Additionally, MdWOX11 reduces the binding of MdWRKY75 to the MdSAUR15 promoter, and alleviates the MdWRKY75-mediated inhibitory effect on MdSAUR15 expression. Collectively, our study provides a MdWOX11-MdWRKY75-MdSAUR15 module regulating root adaptation in response to salt stress in apple.

Malus↗

PubMed2025

Immune Cell Type-Specific DNA Methylation Regions Associate With 24-Hour Blood Pressure Regulation in Black People.

BACKGROUND: DNA methylation and immune cells have been linked to blood pressure (BP) regulation and the development of hypertension. However, the immune cell profiles and the cell type-specific DNA methylation associated with BPs remain unclear. METHODS: This study evaluates the 19 cell type deconvolution algorithms using reduced representation bisulfite sequencing data, comparing them to in silico mixtures derived from whole-genome bisulfite sequencing. The top-performing algorithm, Epigenetic Dissection of Intra-Sample Heterogeneity (EpiDISH)-Robust Partial Correlations, was applied to 281 Black inpatients with 24-hour BP monitoring. The immune cell profiles and cell type-specific DNA methylation regions associated with these BP phenotypes were further investigated using regression analysis. RESULTS: In patients with hypertension, B-cell and CD4 effector memory T-cell abundances were significantly elevated. Monocyte and CD8 effector memory T-cell fractions positively correlated with nighttime BP, and CD3 T cells were inversely associated with office BP. These associations remained robust after covariate adjustments and were partially validated in the Medical Information Mart for Intensive Care-IV cohort. For the first time, we identified several cell type-specific DNA methylation regions as being associated with BP phenotypes and patterns across 13 immune cells, with approximately one third predominantly found in effector CD8 T cells. CONCLUSIONS: These findings provide novel insights into the epigenetically regulated immune mechanisms underlying BP regulation and identify potential targets for hypertension management.

Humans↗

PubMed2025

Knowledge-guided Contextual Gene Set Analysis Using Large Language Models.

Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conventional GSA methods overlook clinical context of the analyses, often generating long lists of enriched pathways with redundant, nonspecific, or irrelevant results. Interpreting these requires extensive, ad-hoc manual effort, reducing both reliability and reproducibility. To address this limitation, we introduce cGSA, a novel AI-driven framework that enhances GSA by incorporating context-aware pathway prioritization. cGSA integrates gene cluster detection, enrichment analysis, and large language models to identify pathways that are not only statistically significant but also biologically meaningful. Benchmarking on 102 manually curated gene sets across 19 diseases and ten disease-related biological mechanisms shows that cGSA outperforms baseline methods by over 30%, with expert validation confirming its increased precision and interpretability. Two independent case studies in melanoma and breast cancer further demonstrate its potential to uncover context-specific insights and support targeted hypothesis generation.

Journal Article↗

PubMed2024

Mediating effects of BMI on the association between DNA methylation regions and 24-h blood pressure in African Americans.

BACKGROUND: DNA methylation is an important epigenetic mechanism that may influence blood pressure (BP) regulation and hypertension risk. Obesity, a major lifestyle factor associated with hypertension, may interact with DNA methylation to affect BP. However, the indirect effect of DNA methylation on 24-h BP measurements mediated by obesity-related phenotypes such as BMI has not been investigated. METHODS: Causal mediation analysis was applied to examine the mediating role of BMI in the relation between DNA methylation and 24-h BP phenotypes, including SBP, DBP and mean arterial blood pressure (MAP), in 281 African American participants. RESULTS: Analysis of 38 215 DNA methylation regions, derived from 1 549 368 CpG sites across the genome, identified up to 138 methylation regions that were significantly associated with 24-h BP measurements through BMI mediation. Among them, 38 (19.2%) methylation regions were concurrently associated with SBP, DBP and MAP. Genes associated with BMI-mediated methylation regions are potentially involved in various chronic diseases such as coronary artery disease and renal disease, which are often caused or exacerbated by hypertension. Notably, three genes ( CDH4 , NOTCH1 and COLGALT1 ) showed both direct associations with 24-h BP measurements and indirect associations through BMI after adjusting for age and sex covariates. CONCLUSION: Our findings suggest that DNA methylation may contribute to the regulation of 24-h BP in African Americans both directly and indirectly through BMI mediation.

Humans↗