| MoleculeNet: a benchmark for molecular machine learning Z Wu, B Ramsundar, EN Feinberg, J Gomes, C Geniesse, AS Pappu, ... Chemical science 9 (2), 513-530, 2018 | 2369 | 2018 |
| PotentialNet for molecular property prediction EN Feinberg, D Sur, Z Wu, BE Husic, H Mai, Y Li, S Sun, J Yang, ... ACS central science 4 (11), 1520-1530, 2018 | 335 | 2018 |
| Is multitask deep learning practical for pharma? B Ramsundar, B Liu, Z Wu, A Verras, M Tudor, RP Sheridan, V Pande Journal of chemical information and modeling 57 (8), 2068-2076, 2017 | 257 | 2017 |
| Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells RT Leenay, A Aghazadeh, J Hiatt, D Tse, TL Roth, R Apathy, E Shifrut, ... Nature biotechnology 37 (9), 1034-1037, 2019 | 108 | 2019 |
| Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy B Ramsundar, P Eastman, P Walters, V Pande, K Leswing, Z Wu Drug Discovery, and More 1, 2019 | 107 | 2019 |
| Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens Z Wu, AE Trevino, E Wu, K Swanson, HJ Kim, HB D’Angio, R Preska, ... Nature Biomedical Engineering 6 (12), 1435-1448, 2022 | 50* | 2022 |
| DynaMorph: self-supervised learning of morphodynamic states of live cells Z Wu, BB Chhun, G Popova, SM Guo, CN Kim, LH Yeh, T Nowakowski, ... Molecular biology of the cell 33 (6), ar59, 2022 | 22* | 2022 |
| PB-Net: Automatic peak integration by sequential deep learning for multiple reaction monitoring Z Wu, D Serie, G Xu, J Zou Journal of proteomics 223, 103820, 2020 | 18 | 2020 |
| Leveraging physiology and artificial intelligence to deliver advancements in health care A Zhang, Z Wu, E Wu, M Wu, MP Snyder, J Zou, JC Wu Physiological Reviews 103 (4), 2423-2450, 2023 | 14 | 2023 |
| 7-UP: Generating in silico CODEX from a small set of immunofluorescence markers E Wu, AE Trevino, Z Wu, K Swanson, HJ Kim, HB D’Angio, R Preska, ... PNAS nexus 2 (6), pgad171, 2023 | 12 | 2023 |
| Determination of equilibrium constant and relative brightness in fluorescence correlation spectroscopy by considering third-order correlations Z Wu, H Bi, S Pan, L Meng, XS Zhao The Journal of Physical Chemistry B 120 (45), 11674-11682, 2016 | 8 | 2016 |
| Predicting target genes of non-coding regulatory variants with IRT Z Wu, NM Ioannidis, J Zou Bioinformatics 36 (16), 4440-4448, 2020 | 6 | 2020 |
| Deep learning for biomedical videos: perspective and recommendations D Ouyang, Z Wu, B He, J Zou Artificial Intelligence in Medicine, 37-48, 2021 | 3 | 2021 |
| Identifying spatial cellular structures with SPACE-GM Z Wu Nature Reviews Cancer 23 (8), 508-508, 2023 | | 2023 |
| Spatial proteomics of human diabetic kidney disease, from health to class III A Kondo, M McGrady, D Nallapothula, H Ali, AE Trevino, A Lam, R Preska, ... bioRxiv, 2023.04. 12.534028, 2023 | | 2023 |
| A Multi-Granularity Approach to Similarity Search in Multiplexed Immunofluorescence Images J Yu, Z Wu, AT Mayer, A Trevino, J Zou bioRxiv, 2023.11. 26.568745, 2023 | | 2023 |
| PEPSI: Polarity measurements from spatial proteomics imaging suggest immune cell engagement E Wu, Z Wu, AT Mayer, AE Trevino, J Zou PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024, 492-505, 2023 | | 2023 |
| Characterizing tissue structures from spatial omics with spatial cellular graph partition Z Wu, A Kondo, M McGrady, EAG Baker, E Wu, MK Rahim, NA Bracey, ... bioRxiv, 2023.09. 05.556133, 2023 | | 2023 |
| Learning spatial cellular motifs predictive of the responses of patients to cancer treatments Z Wu, J Zou NATURE BIOMEDICAL ENGINEERING 6 (12), 1328-1329, 2022 | | 2022 |
| Deep Learning in Computational Biology: From Predictive Modeling to Knowledge Extraction Z Wu Stanford University, 2022 | | 2022 |
