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
• Porject Layout
• Authors
• License

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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

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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
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├── 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

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Authors

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

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License

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