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deep learning experiments on biological sequences with PyTorch

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Biological Sequence Learning

This project contains deep learning experiments on biological sequences (amino acid or nucleotide sequences), focusing in the learning of ultra-long sequences, and the biological context and hierarchy of the sequences. Please refer to the REPORT file for more technical details and weekly progress.

Table of Contents


Getting Started

This project uses Poetry for dependency management. Check 'pyproject.toml' for auto-generated dependencies.

conda install poetry  # or 'pip install poetry'
poetry install

Project Layout

The project layout with the usage for each folder is shown below:

dl-project-template
.
|
├── LICENSE.md
├── README.md
├── DESCRIPTION.md      # project and experiment description
├── makefile            # makefile for various commands (install, train, pytest, mypy, lint, etc.) 
├── mypy.ini            # MyPy type checking configurations
├── pylint.rc           # Pylint code quality checking configurations
├── pyproject.toml      # Poetry project and environment configurations
|
├── data
|   ├── ...             # data reference files (index, readme, etc.)
│   ├── raw             # untreated data directly downloaded from source
│   ├── interim         # intermediate data processing results
│   └── processed       # processed data (features and targets) ready for learning
|
├── notebooks           # Jupyter Notebooks for data processing experiments and visualization
├── scripts             # shell/Python scripts for data downloading. pre-processing, and visualization
│── src    
│   │── ...             # top-level scripts for training, testing and global tasks
│   ├── configs         # configuration files for deep learning experiments
│   ├── datasets        # dataset classes and other processing functions
│   ├── modules         # activations, layers, modules, and networks (subclass of torch.nn.Module)
│   ├── optimization    # deep learning optimizers and schedulers
│   └── utilities       # other useful functions and classes
├── tests               # unit tests module for ./src
│
├── docs                # documentation files (*.txt, *.doc, *.jpeg, etc.)
├── logs                # logs for deep learning experiments
└── models              # saved models with optimizer states

Authors

  • Xiaotian Duan (Email: xduan7 at gmail.com)

License

This project is licensed under the MIT License - check the LICENSE file for more details.