简介
目前，李苓玉是香港大学生物医学学院的博士后研究员，合作导师是黄渊华副教授，研究方向为生物信息学。她现为威康桑格研究所访问博士后（2026年10月-2027年3月），与Omer Bayraktar博士合作，开展胶质母细胞瘤的计算分析。她在山东大学控制科学与工程学院获得博士学位（学术型），指导教师是刘治平教授。此外，她还曾经是香港大学数学系的一名联合培养博士生（2021年12月-2023年3月），合作导师是程瑋琪教授。
兴趣爱好
- 生物信息学
- 生物标志物识别
- 基因调控网络推断
- 稀疏统计学习算法
- 空间转录组学分析
教育经历
工学博士，生物医学工程, 2019.09-2023.06
山东大学 (SDU)
理学硕士，计算数学, 2016.09-2019.06
山东师范大学 (SDNU)
理学学士，数学与应用数学, 2012.09-2016.06
山东师范大学 (SDNU)
教育经历
论文
(2026).
Cell dynamics on energy landscape: Comparing attractor detection in Boolean network and diffusion-based models under in silico gene perturbations.
In QUANT BIOL.
(2026).
CNet-Cox for interpretable network biomarker discovery and survival risk scoring in precise breast cancer prognosis.
In npj Digit Med.
(2026).
FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.
In NAT COMMUN.
(2026).
Prognostic biomarker discovery via a connected network-constrained Cox proportional hazards model.
In BRIEF BIOINFORM.
(2026).
SparseAEH: Scalable autoregressive expression histology discovery in spatial transcriptomics via sparse Gaussian kernels.
In BioRxiv.
(2025).
getDNB: identifying dynamic network biomarkers from time-varying gene regulations utilizing graph embedding techniques.
In BRIEF BIOINFORM.
(2025).
NetWalkRank: Cancer driver gene prioritization in multiplex gene regulatory networks by a random walk approach.
In IEEE ACM T COMPUT BI.
(2023).
Biomarker discovery from high-throughput data by connected network-constrained support vector machine.
In EXPERT SYST APPL.
(2023).
LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data .
In BIOINFORMATICS.
(2023).
Identifying diagnostic biomarkers of breast cancer based on gene expression data and ensemble feature selection.
In CURR BIOINFORM.
(2022).
scGENA: A single-cell gene coexpression network analysis framework for clustering cell types and revealing biological mechanisms.
In Bioengineering.
(2022).
Robust biomarker screening from gene expression data by stable machine learning-recursive feature elimination methods.
In COMPUT BIOL CHEM.
(2022).
A connected network-regularized logistic regression model for feature selection.
In APPL INTELL.
(2021).
Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models.
In J TRANSL MED.
(2021).
Discovery of spontaneous preterm birth biomarkers based on machine learning.
In Journal of Nanjing University(Natural Sciences).
(2020).
Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression.
In COMPUT STRUCT BIOTEC.
(2020).
Compact finite-difference method for 2D time-fractional convection–diffusion equation of groundwater pollution problems.
In COMPUT APPL MATH.
(2018).
Fourth-order compact finite difference method for solving two-dimensional convection–diffusion equation.
In ADV DIFFER EQU-NY.
(2017).
Numerical simulation of groundwater pollution problems based on convection diffusion equation.
In AJCM.
(2016).
Uniform convergence and application of function term's series.
In Journal of Shandong Normal University (Natural Science).
联系
- lingyuli@hku.hk
- Room 1-05, 5 Sassoon Road, Hong Kong SAR,
- Jockey Club Interdisciplinary Research Building
- Monday - Friday 09:00 to 19:00