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arXiv · 2109.12204

MIIDL: a Python package for microbial biomarkers identification powered by interpretable deep learning

Jian Jiang
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Abstract

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.

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Jian Jiang. 2021-09-24. MIIDL: a Python package for microbial biomarkers identification powered by interpretable deep learning. https://arxiv.org/abs/2109.12204

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