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

• URL: http://arxiv.org/abs/2109.12204v1
• Date: Fri, 24 Sep 2021 21:30:10 GMT
• Title: MIIDL: a Python package for microbial biomarkers identification powered by interpretable deep learning
• Authors: Jian Jiang
• Abstract summary: 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.
• Score: 5.749346757892117
• License: http://creativecommons.org/licenses/by/4.0/
• 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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