AMAnD: an automated metagenome anomaly detection methodology utilizing DeepSVDD neural networks

AMAnD: an automated metagenome anomaly detection methodology utilizing DeepSVDD neural networks

The composition of metagenomic communities within the human body often reflects localized medical conditions such as upper respiratory diseases and gastrointestinal diseases. Fast and accurate computational tools to flag anomalous metagenomic samples from typical samples are desirable to understand...

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Bibliographic Details
Main Authors:	Colin Price, Joseph A. Russell
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2023-07-01
Series:	Frontiers in Public Health
Subjects:	anomaly detection metagenomics deep learning DeepSVDD machine learning
Online Access:	https://www.frontiersin.org/articles/10.3389/fpubh.2023.1181911/full

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