Bo Li 李波 — AI for Biology: from multimodal virtual cells to experimental decisions
I build AI methods that turn multimodal perturbation data into better experimental decisions. My research connects reliable measurement, cellular response modeling, and intervention design to identify which biological effects we can predict and which interventions are worth testing.
🔥 News
🔬 Research
What biological distinctions and predictive capabilities can we reliably evaluate?
Determine what biological distinctions experiments can reproducibly resolve and what response structure predictive models actually recover. Separate measurement limits, aggregate performance, and context-specific recovery before interpreting benchmark results.
What biological information transfers across modalities and contexts?
Learn and evaluate cellular representations across transcriptomic, morphological, spatial, and perturbational measurements, with an emphasis on what information remains useful under biological and experimental shifts.
Which experiments are worth performing next?
Formulate intervention design as a finite-budget decision problem: rank feasible interventions toward a biological objective and evaluate the selected experiments using independently measured outcomes.
Adaptive intervention design: connect virtual-cell models and scientific agents with independent experimental feedback, so each round of experiments informs the next.
📝 Selected Publications
VCDesign: Finite-Budget Intervention Design for Virtual Cells
Bo Li, Lin Wang, Bob Zhang, Mengran Li, Zhenchao Tang, Chengyang Zhang, Minghao Sun, Chengliang Liu, Zhiyuan Liu, Yang Zhang
Manuscript 2026 · Project · Manuscript · Code · Data
TL;DR: Ranks feasible interventions under a limited experimental budget, using historical perturbation data and biological knowledge to prioritize response-unseen candidates. Evaluates the selected experiments against independently measured outcomes.
Measurement resolution constrains fine-grained perturbation prediction
Bo Li, Chengyang Zhang, Mengran Li, Bob Zhang, Lin Wang, Zhenchao Tang, Jun Liu, Chengliang Liu, Chen Wei, Yuhao Yi, Jiancheng Lv, Yang Zhang
Manuscript 2026 · Project · Manuscript · Code · Data
TL;DR: Separates measurement limits from modeling limits by testing whether experiments can reproducibly distinguish fine-grained perturbation effects, and whether predictive models recover those distinctions rather than only shared response patterns.
DrugDis: Disentangling general and context-specific effects in drug-response prediction
Bo Li, Chengliang Liu, Yuzhong Peng, Bob Zhang, Qing Wang, Pinxian Zeng, Mengran Li, Shenghui Huang, Chuxia Deng, Yang Zhang
Manuscript 2026 · Project · Manuscript · Supplement · Code · Data
TL;DR: Separates general drug and sample effects from context-specific interactions, revealing what drives aggregate prediction accuracy and whether model improvements recover the response components needed for biological interpretation.
MVCBench: A Multimodal Benchmark for Drug-induced Virtual Cell Phenotypes
Bo Li, Qing Wang, Shihang Wang, Bob Zhang, Yuzhong Peng, Pinxian Zeng, Chengliang Liu, Mengran Li, Ziyang Tang, Xiaojun Yao, Chuxia Deng, Qianqian Song
bioRxiv 2026 · Project · Preprint · Code · Data
TL;DR: Benchmarks molecular and cellular representations for drug-induced transcriptomic and morphological responses, testing which predictive gains persist across compounds, cell lines, experimental batches, and datasets.
PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery
Bo Li, Bob Zhang, Chengyang Zhang, Minghao Zhou, Weiliang Huang, Shihang Wang, Qing Wang, Mengran Li, Yong Zhang, Qianqian Song
Nature Communications 17, 793 (2026) · Paper · Webserver · Code · arXiv
TL;DR: Learns cellular representations directly from high-content microscopy for phenotypic profiling and drug discovery, connecting image-derived features to biological differences between diverse cellular perturbations.
SpaIM: Single-cell Spatial Transcriptomics Imputation via Style Transfer
Bo Li, Ziyang Tang, Aishwarya Budhkar, Xiang Liu, Tonglin Zhang, Baijian Yang, Jing Su, Qianqian Song
Nature Communications 16, 7861 (2025) · Paper · Code · Data
TL;DR: Integrates single-cell and spatial transcriptomics through style-transfer imputation, predicting spatial gene-expression patterns to connect complementary measurements of cellular identity and tissue organization.
🛠 Software & Resources
Selected research software and community resources.
| Project | What it is | Stars |
|---|---|---|
| VCDesign | Finite-budget intervention ranking and decision-aligned evaluation for virtual cells | 3 |
| PertResolve | Measurement-aware evaluation framework for interpreting perturbation-prediction performance | 3 |
| DrugDis | Component-resolved evaluation of general and context-specific drug-response prediction | 1 |
| MVCBench | Multimodal benchmark for virtual-cell representation and generalization | 10 |
| PhenoProfiler | End-to-end phenotypic representation learning for high-content cell imaging | 11 |
| Awesome-Virtual-Cell | Curated literature, datasets, benchmarks, and resources for virtual-cell research | 315 |
| Nature-Paper-Skills | Agent skills for scientific manuscript drafting, revision, auditing, and resubmission | 529 |
| SpaIM | Cross-modal modeling for spatial transcriptomics imputation | 23 |
Stars as of Oct 2, 2026 · Links open current GitHub counts.
📖 Education
-
2026.06 – 2027.06: National University of Singapore
- Visiting Student. Host: Prof. Yang Zhang
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2024.08 – Present: University of Macau
- Ph.D. in Computer Science, Department of Artificial Intelligence, Full Scholarship. Advisor: Prof. Bob Zhang; Co-advisor: Prof. Qianqian Song, Purdue University
-
2021.09 – 2024.07: Beijing University of Technology
- M.Eng. in Electronic Information. Advisors: Prof. Yong Zhang, Prof. Baocai Yin
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2017.09 – 2021.07: Beijing Information Science & Technology University
- B.Eng. in Robotics Engineering. Advisor: Prof. Hongbo Huang
🎖 Selected Honors & Awards
- 2024: Ph.D. Scholarship, University of Macau
- 2024.07: Top 100 Graduates of BJUT (Top 100 / 6331)
- 2024.07 & 2021.07: Beijing Outstanding Graduate
- 2023.10: Xiaomi Scholarship
- 2022.10: National Scholarship
- 2021: First Prize, Science & Technology Innovation Scholarship, BISTU
📜 Patents
Co-inventor of three Chinese invention patents on cell image density map generation and cell localization: CN115457546A, CN115810046A, CN115457547A.
💼 Academic Service
Journal reviewer: Science Advances, IEEE TPAMI, IEEE TIP, IEEE TNNLS, Medical Image Analysis, Bioinformatics, Briefings in Bioinformatics, Pattern Recognition, BMC Biology, Engineering Applications of Artificial Intelligence, and Expert Systems with Applications.
Collaborations: Purdue University, Cornell University, University of Florida, National University of Singapore, Sun Yat-sen University, Sichuan University, Beijing University of Technology, and Macao Polytechnic University.
🧭 Research Interests & Technical Stack
Technical: Python · PyTorch · CUDA · Linux
