https://pypi.org/project/synthetic-models/ Recent updates to the Python Package Index for synthetic-models en https://pypi.org/project/synthetic-models/0.1.3/ synthetic-models is a virtual cell generation library for generating biological synthetic data that can be used for benchmarking predictive modelling workflows. The synthetic data are generated using ODE models formulated in biochemical laws common in cancer cell signalling networks. Synthetic can create datasets in the format of scikit-learn's make_regression method. 47137689+AnEvilBurrito@users.noreply.github.com Mon, 30 Mar 2026 02:00:12 GMT https://pypi.org/project/synthetic-models/0.1.2/ synthetic-models is a virtual cell generation library for generating biological synthetic data that can be used for benchmarking predictive modelling workflows. The synthetic data are generated using ODE models formulated in biochemical laws common in cancer cell signalling networks. Synthetic can create datasets in the format of scikit-learn's make_regression method. 47137689+AnEvilBurrito@users.noreply.github.com Mon, 30 Mar 2026 01:25:57 GMT https://pypi.org/project/synthetic-models/0.1.1/ synthetic-models is a virtual cell generation library for generating biological synthetic data that can be used for benchmarking predictive modelling workflows. The synthetic data are generated using ODE models formulated in biochemical laws common in cancer cell signalling networks. Synthetic can create datasets in the format of scikit-learn's make_regression method. 47137689+AnEvilBurrito@users.noreply.github.com Thu, 26 Mar 2026 04:54:42 GMT https://pypi.org/project/synthetic-models/0.1.0/ synthetic-models is a virtual cell generation library for generating biological synthetic data that can be used for benchmarking predictive modelling workflows. The synthetic data are generated using ODE models formulated in biochemical laws common in cancer cell signalling networks. Synthetic can create datasets in the format of scikit-learn's make_regression method. 47137689+AnEvilBurrito@users.noreply.github.com Thu, 26 Mar 2026 04:45:25 GMT