MIIDL /ˈmaɪdəl/
is a Python package for microbial biomarkers identification powered by interpretable deep learning.
👋Welcome!
🔗This guide will provide you with a specific example that using miidl
to detect microbial biomarkers of colorectal cancer and predict clinical outcomes.
After that, you will learn how to use this tool properly.
pip install miidl
or
conda install miidl captum -c pytorch -c conda-forge -c bioconda
- One-stop profiling
- Multiple strategies for biological data
- More interpretable, not a "black box"
The very first procedure is filtering features according to a threshold of observation (non-missing) rate (0.3 by default).
miidl
offers plenty of normalization methods to transform data and make samples more comparable.
By default, this step is inactivated, as miidl
is designed to solve problems including sparseness. But imputation can be useful in some cases. Commonly used methods are available if needed.
The pre-processed data also need to be zero-completed to a certain length, so that a CNN model can be applied.
A CNN classifier is trained for discrimination. PyTorch is needed.
Captum is dedicated to model interpretability for PyTorch. This step depends heavily on captum.
If you have further thoughts or queries, please feel free to email at jianjiang.bio@gmail.com or open an issue!
@misc{jiang2021miidl,
title={MIIDL: a Python package for microbial biomarkers identification powered by interpretable deep learning},
author={Jian Jiang},
year={2021},
eprint={2109.12204},
archivePrefix={arXiv},
primaryClass={q-bio.QM}
}
MIIDL is released under the MIT license.