Cancer

Cancer is one of the leading causes of illness and death in Singapore. Common cancers such as breast, colorectal, lung, cervical, and prostate cancer place a significant burden on individuals, families, and the healthcare system. 

Cancer is often more treatable when found early. Screening, medical imaging, and awareness of symptoms can greatly improve survival and quality of life.

Research into effective screening helps identify these cancers at an early stage for our ageing population, when treatment is more likely to be successful and less costly. 

Drug development is another critical research thrust that will positively impact disease treatment and care.

Overall, cancer screening is a critical research focus in Singapore because it saves lives through early detection, supports healthy ageing, improves healthcare efficiency, and aligns with national goals in preventive medicine and biomedical innovation.

Sub-theme:
- Screening and diagnosis
- Drug-development
1. Harnessing AI for Enhanced Cancer Prevention in Asia. PI: Assoc. Prof. Joanne Ngeow

2. Immune-Stimulating Anti-Cancer RNA Lipid Nanoparticle Design with Deep Learning. PI: Asst. Prof. Alvin Chan

3. BRnAIN: Targeted RNA Carrier Design for Brain Tissues with Deep Learning and High-throughput In-Vivo Screening. PI: Asst. Prof Alvin Chan

4. Lipid nanoparticle platform technology for targeted mRNA delivery. Co-PI: Asst. Prof Alvin Chan



1. Zhang, T., Fang, W., Woo, J., Latawa, P., Subramanian, D. A., & Chan, A. (2025). Can LLMs Reason Over Non-Text Modalities in a Training-Free Manner? A Case Study with In-Context Representation Learning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2509.17552

2. Fang, W., Zhang, T., & Chan, A. (2025). To align or not to align: strategic multimodal representation alignment for optimal performance. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2511.12121

3. Chan, A., Kirtane, A. R., Qu, Q. R., Huang, X., Woo, J., Subramanian, D. A., Dey, R., Semalty, R., Bernstock, J. D., Ahmed, T., Honeywell, R., Hanhurst, C., Becdach, I. D., Prizant, L. S., Brown, A. K., Song, H., Cobb, J. L., DeRidder, L. B., Santos, B., . . . Traverso, G. (2025). Designing lipid nanoparticles using a transformer-based neural network. Nature Nanotechnology, 20(10), 1491–1501. https://doi.org/10.1038/s41565-025-01975-4