CV

Research Profile

Yuxiang Lin
Xiamen University

  • Research interests: single-cell and spatial omics, tumor microenvironment, biomedical agents, cancer biomarker discovery, perturbation-response modeling, proteomics, multi-omics data mining.
  • Email: linyuxiang@stu.xmu.edu.cn
  • Public profiles: Google Scholar, ORCID.
  • Google Scholar metrics: 144 citations, h-index 6, i10-index 5 as of September 9, 2026.

Selected Publications

  • SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types. Nature Cancer, 2026.
  • Chem2Gen-Bench: Benchmarking Chemical-to-Genetic Translation in Perturbation Response Space. arXiv, 2026.
  • ST-Align: Multi-Scale Image-Gene Foundation Modeling for Spatial Transcriptomics via Spot-Niche Alignment. ICLR 2026 Workshop on Foundation Models for Science, 2026.
  • TiRank prioritizes phenotypic niches in tumor microenvironment for clinical biomarker discovery. Genome Medicine, 2026.
  • HyperST: Hierarchical Hyperbolic Learning for Spatial Transcriptomics Prediction. CVPR 2026 Spotlight, 2026.
  • BioMTAN: A Biological Knowledge-guided Multi-task Attention Network for Co-enhanced Cancer Diagnosis and Prognosis. IEEE Journal of Biomedical and Health Informatics, 2025.
  • SurvMamba: State Space Model with Multi-Grained Multi-Modal Interaction for Survival Prediction. IEEE BIBM, 2025.
  • Tracing unknown tumor origins with a biological-pathway-based transformer model. Cell Reports Methods, 2024.
  • PhosMap: An ensemble bioinformatic platform to empower interactive analysis of quantitative phosphoproteomics. Computers in Biology and Medicine, 2024.
  • SIMarker: Cellular similarity detection and its application to diagnosis and prognosis of liver cancer. Computers in Biology and Medicine, 2024.
  • Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer. Briefings in Bioinformatics, 2023.

Methods and Topics

  • Computational biology and bioinformatics
  • Biomedical agents and machine learning
  • Perturbation-response modeling and benchmark construction
  • Single-cell and spatial transcriptomics
  • Tumor microenvironment analysis
  • Proteomics and phosphoproteomics
  • Cancer diagnosis, prognosis, and biomarker discovery