Chapter title |
Predicting Host Phenotype Based on Gut Microbiome Using a Convolutional Neural Network Approach.
|
---|---|
Chapter number | 12 |
Book title |
Artificial Neural Networks
|
Published in |
Methods in molecular biology, January 2021
|
DOI | 10.1007/978-1-0716-0826-5_12 |
Pubmed ID | |
Book ISBNs |
978-1-07-160825-8, 978-1-07-160826-5
|
Authors |
Reiman, Derek, Farhat, Ali M, Dai, Yang, Derek Reiman, Ali M. Farhat, Yang Dai, Farhat, Ali M. |
Abstract |
Accurate prediction of the host phenotypes from a microbial sample and identification of the associated microbial markers are important in understanding the impact of the microbiome on the pathogenesis and progression of various diseases within the host. A deep learning tool, PopPhy-CNN, has been developed for the task of predicting host phenotypes using a convolutional neural network (CNN). By representing samples as annotated taxonomic trees and further representing these trees as matrices, PopPhy-CNN utilizes the CNN's innate ability to explore locally similar microbes on the taxonomic tree. Furthermore, PopPhy-CNN can be used to evaluate the importance of each taxon in the prediction of host status. Here, we describe the underlying methodology, architecture, and core utility of PopPhy-CNN. We also demonstrate the use of PopPhy-CNN on a microbial dataset. |
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