Research

Our Research Focus

The Joe Yeong Laboratory is dedicated to advancing the field of spatial omics through innovative research and computational approaches.

Current Research Areas

Spatial Transcriptomics

We develop and apply cutting-edge spatial transcriptomics technologies to understand gene expression patterns in their native tissue context.

Multi-omics Integration

Our laboratory specializes in integrating multiple omics datasets to provide comprehensive insights into biological systems.

Computational Biology

We develop novel computational methods and algorithms for analyzing complex spatial omics data.

Biomarker Discovery

Through our research, we identify and validate biomarkers for various diseases and biological processes.

Methodologies

• Single-cell and spatial transcriptomics
• Computational data analysis
• Machine learning applications
• Statistical modeling
• Bioinformatics pipeline development

Collaborations

We actively collaborate with researchers and institutions worldwide to advance our understanding of spatial biology.

Funding

Our research is supported by various funding agencies and institutions (Awarded >20M since 2017). Awarded project funding includes:

• NMRC LCG - Conquering Lung cancer Across all stages with Research and InnovatiON (CLARION)(Role: Theme PI)
• NMRC LCG - Singapore lYMPHoma translatiONal studY (SYMPHONY) 2.0 (Role: Theme PI)
• NMRC CIA - Developing an AI deep learning-based paThology solution for predictive biomArker predictioN for oncoloGy clinical trials allocation (DATANG) (Role: Lead PI)
• NRF CRP - NICHES: NIChe-driven immunoevasion and metastasis in High-risk cancer subtypes for Effective Stratification (Role: Team PI)
• NIH R01 - The roles of EBV-specific T cells in response to checkpoint blockade immunotherapy of EBV-driven nasopharygeal carcinoma (Role: Co-PI)
• MOH CSA - Elucidating ImmuNe ReprogrammIng in NasopharynGeal CarcinoMA (ENIGMA) (Role: Co-PI)
• A*STAR IAF-ICP - Systematic single-cell resolution analyses of the circulating celel milieu in cancer patients (Role: Lead PI)
• A*STAR IAF-ICP - To Develop an AI-powered, Scalable and High-throughput Imaging-based Assay for Identification of Immunogenic Tumour-associated Antigens for Cancer Vaccines (Role: Lead-PI)

---

Publications

Recent Publications

1. Spatial immune scoring system predicts hepatocellular carcinoma recurrence. Nature
2. Mixture of experts in large language models. arXiv preprint
3. A Foundation Model for Spatial Proteomics. arXiv preprint
4. The Hurdle of Precision Medicine in Cancer Immunotherapy: Personalization Now or Then? Cancer Personalized Treatment
5. An integrated approach for analyzing spatially resolved multi-omics datasets from the same tissue section. Frontiers in Molecular Biosciences
6. Asia's emergence in cancer immunotherapy: challenges and opportunities. Journal for ImmunoTherapy of Cancer
7. Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy. Response/Resistance to PD-1 Axis Inhibitors
8. Advancing Tissue Biology Research With Weave Software For Spatial Multi-Omics. MDPI
9. H&E 2.0: deep learning prediction of lung cancer biomarkers pan-cytokeratin and PD-L1 using a dual model framework. Journal for ImmunoTherapy of Cancer
10. A Novel Manual "Centrifuged-Enhanced" Cytosmear Technique for Improving Hypocellular Cytology in the Diagnosis of Vitreoretinal Lymphoma. Translational Vision Science & Technology
11. Charting New Paths in Cancer Research: Insights from the Frontiers in Cancer Science Conference 2024. Cancer Research
12. Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models. arXiv preprint
13. Developing cell-based therapies for pancreatic ductal adenocarcinoma. The Journal of Clinical Investigation
14. Single‐Cell Profiling: Any Scale, Any Size, All at Once. Advanced Science

For the complete list of publications, please visit Prof. Joe Yeong's Google Scholar profile.