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Supervised modelling on genotype, tabular, sequence, image and binary data.

WARNING: This project is in alpha phase. Expect backwards incompatiable changes and API changes.

Install

pip install eir-dl

Usage

Please refer to the Documentation for examples and information.

Use Cases

EIR allows for training and evaluating various deep-learning models directly from the command line. This can be useful for:

• Quick prototyping and iteration when doing supervised modelling on new datasets.
• Establishing baselines to compare against other methods.
• Fitting on data sources such as large-scale genomics, where DL implementations are not commonly available.

If you are a ML/DL researcher developing new models, etc., it might not fit your use case. However, it might provie a quick baseline for comparison to the cool stuff you are developing.

Features

• Train models directly from the command line through .yaml configuration files.
• Training on genotype, tabular, sequence, image and binary input data, with various modality-specific settings available.
• Seamless multi-modal (e.g. combining text + image, or any combination of the modalities above) training.
• Train multiple features extractors on the same data source, e.g. combining vanilla transformer, longformer and a pre-trained BERT variant for text classification.
• Supports continuous (i.e., regression) and categorical (i.e., classification) targets.
• Multi-task / multi-label prediction supported out-of-the-box.
• Model explainability for genotype, tabular, sequence and image data built in.
• Computes and graphs various evaluation metrics (e.g., RMSE, PCC and R2 for regression tasks, accuracy, ROC-AUC, etc. for classification tasks) during training.
• Many more settings and configurations (e.g., augmentation, regularization, optimizers) available.

Citation

If you use EIR in a scientific publication, we would appreciate if you could use the following citation:

@article{sigurdsson2021deep,
  title={Deep integrative models for large-scale human genomics},
  author={Sigurdsson, Arnor Ingi and Westergaard, David and Winther, Ole and Lund, Ole and Brunak, S{\o}ren and Vilhjalmsson, Bjarni J and Rasmussen, Simon},
  journal={bioRxiv},
  year={2021},
  publisher={Cold Spring Harbor Laboratory}
}

Acknowledgements

Massive thanks to everyone publishing and developing the packages this project directly and indirectly depends on.