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PyTorch Implementation of LSTM-based dynamical model for predicting species abundance and metabolite concentrations in a heterogeneous community

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Microbiome

PyTorch Implementation of the LSTM-based dynamical model for the paper titled "Deep Learning enables Design of Multifunctional Synthetic Human Gut Microbiome Dynamics"

Proposed LSTM architecture

Requirements

  • PyTorch
  • scikit-learn
  • LIME
  • Jupyter Notebook
  • MATLAB

Usage

We provide multiple notebook files, one each for each set of experiments. The notebooks are self-sufficient and various relevant details have been marked in the files themselves.

Acknowledgements

This research was supported in part by funding from the Army Research Office (ARO) grant number W911NF1910269. RLC was supported in part by an NHGRI training grant to the Genomic Sciences Training Program (T32 HG002760).

Contact

All code related correspondence must be directed to Mayank Baranwal ([email protected]).

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PyTorch Implementation of LSTM-based dynamical model for predicting species abundance and metabolite concentrations in a heterogeneous community

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