  Detecting microbial biomarkers used to predict disease phenotypes and
clinical outcomes is crucial for disease early-stage screening and diagnosis.
Most methods for biomarker identification are linear-based, which is very
limited as biological processes are rarely fully linear. The introduction of
machine learning to this field tends to bring a promising solution. However,
identifying microbial biomarkers in an interpretable, data-driven and robust
manner remains challenging. We present MIIDL, a Python package for the
identification of microbial biomarkers based on interpretable deep learning.
MIIDL innovatively applies convolutional neural networks, a variety of
interpretability algorithms and plenty of pre-processing methods to provide a
one-stop and robust pipeline for microbial biomarkers identification from
high-dimensional and sparse data sets.
