Beijing University of Posts and Telecommunications
I am an undergraduate student in Software Engineering at Beijing University of Posts and Telecommunications (BUPT), expected to graduate in 2027.
My research interests lie at the intersection of AI for Drug Discovery, Precision Medicine, Multimodal Learning in Healthcare, and Single-cell & Omics Data Analysis. I have worked on generative adversarial frameworks for spatial transcriptomics analysis and contrastive learning methods for single-cell omics data clustering.
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Selected Publications (view all )
BiFlow-GAN: Integrating Bidirectional Flows and Cross-Modal Enhancement for Multi-Modal Spatial Transcriptomics
Jingxuan Wang, Rui Guan, Kun Liang, Shiyu Wang, Xin Yang, Xiaodan Cui, Di Hu
IEEE Transactions on Multimedia (Under Review) 2026 JCR Q1
We propose BiFlow-GAN, a bidirectional-flow generative adversarial framework for robust tissue domain identification in spatial transcriptomics. We design a bidirectional cross-modal attention flow (Bi-AF) mechanism with reciprocal Image-to-Gene and Gene-to-Image pathways for dynamic feature refinement. Benchmarked on diverse spatial transcriptomics datasets, significantly outperforming state-of-the-art methods.
BiFlow-GAN: Integrating Bidirectional Flows and Cross-Modal Enhancement for Multi-Modal Spatial Transcriptomics
Jingxuan Wang, Rui Guan, Kun Liang, Shiyu Wang, Xin Yang, Xiaodan Cui, Di Hu
IEEE Transactions on Multimedia (Under Review) 2026 JCR Q1
We propose BiFlow-GAN, a bidirectional-flow generative adversarial framework for robust tissue domain identification in spatial transcriptomics. We design a bidirectional cross-modal attention flow (Bi-AF) mechanism with reciprocal Image-to-Gene and Gene-to-Image pathways for dynamic feature refinement. Benchmarked on diverse spatial transcriptomics datasets, significantly outperforming state-of-the-art methods.
DGAN-MPCC: A Novel Dual-GAN Enhanced Multi-Positive Contrastive Clustering Method for Omics Data
Jingxuan Wang, Jie Yang, M. A. Khan, Poh Loong Yee, Jian Baili, Di Hu
IEEE Journal of Biomedical and Health Informatics 2025 JCR Q1
We propose a dual-GAN framework to enhance both input and latent representations for noisy single-cell data, and design a multi-positive contrastive clustering strategy to model continuous cell-state transitions. Evaluated on multiple real-world single-cell datasets, achieving state-of-the-art clustering performance.
DGAN-MPCC: A Novel Dual-GAN Enhanced Multi-Positive Contrastive Clustering Method for Omics Data
Jingxuan Wang, Jie Yang, M. A. Khan, Poh Loong Yee, Jian Baili, Di Hu
IEEE Journal of Biomedical and Health Informatics 2025 JCR Q1
We propose a dual-GAN framework to enhance both input and latent representations for noisy single-cell data, and design a multi-positive contrastive clustering strategy to model continuous cell-state transitions. Evaluated on multiple real-world single-cell datasets, achieving state-of-the-art clustering performance.