More detailed growth models using inference.
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Updated
Apr 24, 2020 - Python
More detailed growth models using inference.
Dissecting systems serology with a tensor factorization
Study Fc antibody dynamics using a multivalent binding model
A binding-reaction model for the common gamma chain receptor cytokines.
This is to show oscillations in the number of cells in G1 and in G2 phase of cell cycle.
A Multivalent Binding Model for FcgRs
The structure is the message: preserving experimental context through tensor decomposition
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Trafficking model of FcRn to explain the effects of failed release at the cell surface.
Clusters phosphoproteomics data by sequence and abundance dynamics
R code to model and visualize sweat sodium loss across temperature and humidity conditions for passive vs. active interventions.
R package for “Semiparametric Principal Stratification Analysis Beyond Monotonicity” by Jiaqi Tong, Brennan Kahan, Michael O. Harhay, and Fan Li. Statistica Sinica, in press.
Inferring antibody species from systems serology with a mechanistic binding model.
R package for “Model-Robust Standardization in Cluster-Randomized Trials” by Fan Li, Jiaqi Tong, Xi Fang, Chao Cheng, Brennan C. Kahan, and Bingkai Wang. Statistics in Medicine (2025), 44(20–22):e70270.
R code for “Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials” by Guangyu Tong, Jiaqi Tong, Yi Jiang, Denise Esserman, Michael O. Harhay, and Joshua L. Warren. Clinical Trials (2024), 21(4):451–460.
A modeling perspective on cell selective ligands
Reproducible analysis for between-visit cerebrovascular and cardiovascular responses during the cold pressor test
Gas6 signaling model for TAM receptors
R code for “Doubly Robust Estimation and Sensitivity Analysis With Outcomes Truncated by Death in Multi-Arm Clinical Trials” by Jiaqi Tong, Chao Cheng, Guangyu Tong, Michael O. Harhay, and Fan Li. Statistics in Medicine (2025), 44(28–30):e70297.
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