meyer-lab/tensorpack

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Created over 3 years ago
Updated 3 months ago

meyer-lab/tensorpack

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A collection of tensor methods from the Meyer lab.

tHMM

A general Python framework for using hidden Markov models on binary trees or cell lineage trees.

ps-growth-model

More detailed growth models using inference.

mechanismEncoder

Developing patient-specific phosphoproteomic models using mechanistic autoencoders

systemsSerology

Dissecting systems serology with a tensor factorization

FcRegression.jl

Study Fc antibody dynamics using a multivalent binding model

scCP

PARAFAC2 for modeling multi-sample single-cell RNAseq.

Jupyter Notebook

IL4-model

Jupyter Notebook

tensor-impute

Explore imputation methods for tensor data

gc-cytokines

A binding-reaction model for the common gamma chain receptor cytokines.

FcRn-trafficking

Trafficking model of FcRn to explain the effects of failed release at the cell surface.

DrugResponseModel.jl

This is to show oscillations in the number of cells in G1 and in G2 phase of cell cycle.

Jupyter Notebook

gcSolver

C++ solver for the gamma chain cytokines

FcgR-binding

A Multivalent Binding Model for FcgRs

DDMC

Clusters phosphoproteomics data based on a combination of the sequence information and abundance changes over conditions.

analytic-Pf2

Attempting analytical PARAFAC solving for more accurate PARAFAC2 fitting.

asmlab.org

Meyer lab website