Selected Publications
Selected Publications
Embedded Files
Characterizing tissue structures from spatial omics with spatial cellular graph partition
Zhenqin Wu, Ayano Kondo, Monee McGrady, Ethan A. G. Baker, Benjamin Chidester, Eric Wu, Maha K. Rahim, Nathan A. Bracey, Vivek Charu, Raymond J. Cho, Jeffrey B. Cheng, Maryam Afkarian, James Zou, Aaron T. Mayer, and Alexandro E. Trevino.
Cell Reports Methods, 2024.
DOI: 10.1016/j.crmeth.2024.100838, preprint:10.1101/2023.09.05.556133
Code available at: https://gitlab.com/enable-medicine-public/scgp, datasets available at: https://doi.org/10.5281/zenodo.12682727
7-UP: Generating in silico CODEX from a small set of immunofluorescence markers
Eric Wu, Alexandro E Trevino, Zhenqin Wu, Kyle Swanson, Honesty J Kim, H Blaize D’Angio, Ryan Preska, Aaron E Chiou, Gregory W Charville, Piero Dalerba, Umamaheswar Duvvuri, Alexander D Colevas, Jelena Levi, Nikita Bedi, Serena Chang, John Sunwoo, Ann Marie Egloff, Ravindra Uppaluri, Aaron T Mayer, and James Zou.
PNAS Nexus, 2023.
DOI: 10.1093/pnasnexus/pgad171, preprint: 10.1101/2022.06.03.494624, code available at: https://gitlab.com/enable-medicine-public/7-up
Associated Content:
Spotlight talk at The 2022 ICML Workshop on Computational Biology
Zhenqin Wu*, Alexandro E Trevino*, Eric Wu, Kyle Swanson, Honesty J Kim, H Blaize D’Angio, Ryan Preska, Gregory W Charville, Piero D Dalerba, Ann Marie Egloff, Ravindra Uppaluri, Umamaheswar Duvvuri, Aaron T Mayer, and James Zou.
Nature Biomedical Engineering, 2022
DOI: 10.1038/s41551-022-00951-w, preprint: 10.1101/2022.05.12.491707
Code available at: https://gitlab.com/enable-medicine-public/space-gm, datasets available at: https://doi.org/10.5281/zenodo.13179600
Associated Content:
Contributed talk at The 2022 ICML Workshop on Computational Biology
Learning spatial cellular motifs predictive of the responses of patients to cancer treatments, Research Briefing, Nature Biomedical Engineering
Identifying spatial cellular structures with SPACE-GM, Tools of the Trade, Nature Reviews Cancer
DynaMorph: self-supervised learning of morphodynamic states of live cells
Zhenqin Wu*, Bryant B Chhun*, Galina Popova*, Syuan-Ming Guo, Chang N Kim, Li-Hao Yeh, Tomasz Nowakowski, James Zou, and Shalin B Mehta.
Molecular Biology of the Cell, 2022
DOI: 10.1091/mbc.E21-11-0561, preprint: 10.1101/2020.07.20.213074, code available at: https://github.com/mehta-lab/dynamorph
Associated Content:
Presented at The 2021 ICML Workshop on Computational Biology
PB-Net: Automatic peak integration by sequential deep learning for multiple reaction monitoring
Zhenqin Wu, Daniel Serie, Gege Xu, and James Zou.
Journal of Proteomics, 2020
DOI: 10.1016/j.jprot.2020.103820, code available at: https://github.com/miaecle/PB-net
Associated Content:
Spotlight talk at The 2019 ICML Workshop on Computational Biology
InterVenn Biosciences Releases First-Ever Software for AI-Enabled Mass Spec Analysis, businesswire
Clinical Validation of the InterVenn Ovarian Cancer Liquid Biopsy, Clinical Trials, NCBI
PotentialNet for molecular property prediction
Evan N Feinberg, Debnil Sur, Zhenqin Wu, Brooke E Husic, Huanghao Mai, Yang Li, Saisai Sun, Jianyi Yang, Bharath Ramsundar, Vijay S Pande
ACS Central Science, 2018
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu*, Bharath Ramsundar*, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, Vijay Pande
Chemical Science, 2018
DOI: 10.1039/C7SC02664A, preprint: 10.48550/arXiv.1703.00564, code available at: https://github.com/deepchem/deepchem
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