Ling‐Yun Wu

State Grid Corporation of China (China) — China

Biochemistry, Genetics and Molecular Biology · Life Sciences

32h-index5.3kcitations137works2.12yr avg
OpenAlex↗ORCID↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Bioinformatics and Genomic Networks(33), Gene expression and cancer classification(25), Machine Learning in Bioinformatics(22), Genomics and Phylogenetic Studies(17), Protein Structure and Dynamics(15)

Publications136 total

Effectively Preserving Biological Variations in Multi-Batch and Multi-Condition Single-Cell Data Integration
CSIAM transactions on life sciences.·2025Open Access
Inferring Gene Regulatory Networks From Single‐Cell RNA Sequencing Data by Dual‐Role Graph Contrastive Learning
Advanced Science·2025· 1 citedOpen Access
Optimal Scheduling Method of Cascaded Hydropower Plants Considering the Contradiction Between Supply Guarantee and Flood Prevention
2025
Flippable Siamese Differential Neural Network for Differential Graph Inference
2025Open Access
Adaptive segmentation and optimization for efficient centralizer selection and placement in oilfield drilling operations
Expert Systems with Applications·2025
Enhancing Spatial Transcriptomics via Spatially Constrained Matrix Decomposition with EDGES
Advanced Science·2025· 1 citedOpen Access
Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder
Communications Biology·2025Open Access
Toward energy-efficient blockchain system: A game theoretic analysis
Computers & Industrial Engineering·2024· 3 cited
Evolutionary dynamics and regulatory site analysis of AMP family genes in cattle and sheep
International Journal of Biological Macromolecules·2024· 1 cited
Characterisation of the volatile compounds in nine varieties and three breeding selections of celery using GC–IMS and GC–MS
Food Chemistry X·2024· 10 citedOpen Access
Biologically informed machine learning modeling of immune cells to reveal physiological and pathological aging process
Immunity & Ageing·2024· 5 citedOpen Access
Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks
Bioinformatics·2024· 3 citedOpen Access
Biologically informed machine learning modeling of immune cells to reveal physiological and pathological aging process
2024Open Access
An efficient fluorescence sensor based on chitosan-modified graphitic carbon nitride for selective determination of Cu2+ ions
Microchemical Journal·2024· 7 cited
Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis
Briefings in Bioinformatics·2024· 1 citedOpen Access
PathExpSurv: pathway expansion for explainable survival analysis and disease gene discovery
BMC Bioinformatics·2023· 4 citedOpen Access
Author Correction: Supervised learning of high-confidence phenotypic subpopulations from single-cell data
Nature Machine Intelligence·2023· 1 citedOpen Access
Supervised learning of high-confidence phenotypic subpopulations from single-cell data
Nature Machine Intelligence·2023· 11 cited
Efficient identification of Alzheimer’s brain dynamics with Spatial-Temporal Autoencoder: A deep learning approach for diagnosing brain disorders
Biomedical Signal Processing and Control·2023· 9 cited
Supervised learning of high-confidence phenotypic subpopulations from single-cell data
2023· 6 citedOpen Access
page 1 of 7Next →

Frequent Co-authors

Yu Xia(13), Luonan Chen(12), Xiang‐Sun Zhang(12), Yan Xu(10), Yong Wang(9), Jun Ding(7), Nai-Yang Deng(7), Ping Luo(6), Rui‐Sheng Wang(6), Ruimao Zhang(4), Xiaojian Shao(3), Shihua Zhang(3), Qiang Huang(3), Zhi‐Ping Liu(3), Ya-Xin Ding(3), Cheng‐Han Lee(2), Ziwei Liu(2), Duanchen Sun(2), Zheng Xia(2), Kuo‐Chen Chou(2)