| Deep learning in single-cell analysis D Molho, J Ding, W Tang, Z Li, H Wen, Y Wang, J Venegas, W Jin, R Liu, ... ACM Transactions on Intelligent Systems and Technology 15 (3), 1-62, 2024 | 27 | 2024 |
| Graph foundation models H Mao, Z Chen, W Tang, J Zhao, Y Ma, T Zhao, N Shah, M Galkin, J Tang arXiv preprint arXiv:2402.02216, 2024 | 25 | 2024 |
| Graph machine learning in the era of large language models (llms) W Fan, S Wang, J Huang, Z Chen, Y Song, W Tang, H Mao, H Liu, X Liu, ... arXiv preprint arXiv:2404.14928, 2024 | 19 | 2024 |
| Position: Graph Foundation Models Are Already Here H Mao, Z Chen, W Tang, J Zhao, Y Ma, T Zhao, N Shah, M Galkin, J Tang Forty-first International Conference on Machine Learning, 0 | 18 | |
| CellPLM: pre-training of cell language model beyond single cells H Wen, W Tang, X Dai, J Ding, W Jin, Y Xie, J Tang bioRxiv, 2023.10. 03.560734, 2023 | 16 | 2023 |
| Single-cell multimodal prediction via transformers W Tang, H Wen, R Liu, J Ding, W Jin, Y Xie, H Liu, J Tang Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 13 | 2023 |
| Dance: A deep learning library and benchmark for single-cell analysis J Ding, H Wen, W Tang, R Liu, Z Li, J Venegas, R Su, D Molho, W Jin, ... bioRxiv, 2022.10. 19.512741, 2022 | 10 | 2022 |
| Single cells are spatial tokens: Transformers for spatial transcriptomic data imputation H Wen, W Tang, W Jin, J Ding, R Liu, X Dai, F Shi, L Shang, H Liu, Y Xie arXiv preprint arXiv:2302.03038, 2023 | 9 | 2023 |
| DANCE: A deep learning library and benchmark platform for single-cell analysis J Ding, R Liu, H Wen, W Tang, Z Li, J Venegas, R Su, D Molho, W Jin, ... Genome Biology 25 (1), 72, 2024 | 8 | 2024 |
| SpatialCTD: A Large-Scale Tumor Microenvironment Spatial Transcriptomic Dataset to Evaluate Cell Type Deconvolution for Immuno-Oncology J Ding, L Li, Q Lu, J Venegas, Y Wang, L Wu, W Jin, H Wen, R Liu, ... Journal of Computational Biology 31 (9), 871-885, 2024 | 4 | 2024 |
| A general single-cell analysis framework via conditional diffusion generative models W Tang, R Liu, H Wen, X Dai, J Ding, H Li, W Fan, Y Xie, J Tang bioRxiv, 2023.10. 13.562243, 2023 | 4 | 2023 |
| Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models W Tang, H Mao, D Dervovic, I Brugere, S Mishra, Y Xie, J Tang arXiv preprint arXiv:2406.01899, 2024 | 3 | 2024 |
| SpatialCTD: a large-scale TME spatial transcriptomic dataset to evaluate cell type deconvolution for immuno-oncology J Ding, J Venegas, Q Lu, Y Wang, L Wu, W Jin, H Wen, R Liu, W Tang, ... bioRxiv, 2023.04. 11.536333, 2023 | 1 | 2023 |
| Sub-graph Based Diffusion Model for Link Prediction H Li, W Jin, G Skenderi, H Shomer, W Tang, W Fan, J Tang arXiv preprint arXiv:2409.08487, 2024 | | 2024 |
| MEM-GAN: A Pseudo Membrane Generator for Single-cell Imaging in Fluorescent Microscopy Y Wang, J Ding, L Wu, A Wardhani, P Danaher, Q Lu, H Wen, W Tang, ... bioRxiv, 2023.11. 08.566343, 2023 | | 2023 |
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State UniversityAdresă de e-mail confirmată pe msu.edu
