MIIDL: a Python package for microbial biomarkers identification powered by interpretable deep learning
This provides a tool for researchers in microbiology and healthcare to improve disease screening and diagnosis, though it is incremental as it builds on existing deep learning methods.
The authors tackled the problem of identifying microbial biomarkers for disease prediction by developing MIIDL, a Python package that uses interpretable deep learning, resulting in a robust pipeline for handling high-dimensional and sparse datasets.
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.