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paccmann_omics

Generative models of omic data for PaccMann<sup>RL</sup>.

paccmann_omics is a package to model omic data, with examples for generative models of gene expression profiles and encoded proteins (vector representations). For example, see our papers:

Requirements

Installation

The library itself has few dependencies (see setup.py) with loose requirements. To run the example training script we provide environment files under examples/.

Create a conda environment:

conda env create -f examples/gene_expression/conda.yml

Activate the environment:

conda activate paccmann_omics

Install in editable mode for development:

pip install -e .

Example usage

In the examples directory is a training script train_vae.py that makes use of paccmann_omics.

(paccmann_omics) $ python examples/gene_expression/train_vae.py -h
usage: train_vae.py [-h]
                    train_filepath val_filepath gene_filepath model_path
                    params_filepath training_name

Omics VAE training script.

positional arguments:
  train_filepath   Path to the training data (.csv).
  val_filepath     Path to the validation data (.csv).
  gene_filepath    Path to a pickle object containing list of genes.
  model_path       Directory where the model will be stored.
  params_filepath  Path to the parameter file.
  training_name    Name for the training.

optional arguments:
  -h, --help       show this help message and exit

params_filepath could point to examples/gene_expression/example_params.json, examples for other files can be downloaded from here.

References

If you use paccmann_omics in your projects, please cite the following:

@article{born2021datadriven,
  author = {Born, Jannis and Manica, Matteo and Cadow, Joris and Markert, Greta and Mill, Nil Adell and Filipavicius, Modestas and Janakarajan, Nikita and Cardinale, Antonio and Laino, Teodoro and {Rodr{\'{i}}guez Mart{\'{i}}nez}, Mar{\'{i}}a},
  doi = {10.1088/2632-2153/abe808},
  issn = {2632-2153},
  journal = {Machine Learning: Science and Technology},
  number = {2},
  pages = {025024},
  title = {{Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2}},
  url = {https://iopscience.iop.org/article/10.1088/2632-2153/abe808},
  volume = {2},
  year = {2021}
}

@article{born2021paccmannrl,
  title = {PaccMann\textsuperscript{RL}: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning},
  journal = {iScience},
  volume = {24},
  number = {4},
  pages = {102269},
  year = {2021},
  issn = {2589-0042},
  doi = {https://doi.org/10.1016/j.isci.2021.102269},
  url = {https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6},
  author = {Born, Jannis and Manica, Matteo and Oskooei, Ali and Cadow, Joris and Markert, Greta and {Rodr{\'{i}}guez Mart{\'{i}}nez}, Mar{\'{i}}a}
}