Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks
Year: 2019
Type: article
Abstract: Food allergy is usually difficult to diagnose in early life, and the inability to diagnose patients with atopic diseases at an early age may lead to severe complications. Numerous studies have suggested an association between the infant gut microbiome and development of allergy. In this work, we investigated the capacity of Long Short-Term Memory (LSTM) networks to predict food allergies in early life (0-3 years) from subjects' longitudinal gut microbiome profiles. Using the DIABIMMUNE dataset, we show an increase in predictive power using our model compared to Hidden Markov Model, Multi-Layer Perceptron Neural Network, Support Vector Machine, Random Forest, and LASSO regression. We further evaluated whether the training of LSTM networks benefits from reduced representations of microbial features. We considered sparse autoencoder for extraction of potential latent representations in addition to standard feature selection procedures based on Minimum Redundancy Maximum Relevance (mRMR) and variance prior to the training of LSTM networks. The comprehensive evaluation reveals that LSTM networks with the mRMR selected features achieve significantly better performance compared to the other tested machine learning models.
Source: PLoS Computational Biology
Institution: University of Illinois Chicago
Language: English
Subfield: Immunology and Allergy
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,350
Funders: National Institutes of Health, University of Illinois at Urbana-Champaign, National Heart, Lung, and Blood Institute, National Institute of Allergy and Infectious Diseases, National Center for Advancing Translational Sciences, Office of the Vice Chancellor for Research, University of Illinois at Chicago
OpenAlex ID: W2911884040