ArticlePLoS computational biology2019
Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Microbiota Signatures in Early Life and Their Association With Food Allergy: A Systematic Review.Allergy · 2026Pooled it
- AI-empowered human microbiome research.Gut · 2026Review
- An adaptive weight self-distillation deep learning framework for phenotype prediction from longitudinal gut microbiome data.BMC microbiology · 2026Article
- DynaBiome: interpretable unsupervised learning of gut microbiome dysbiosis via temporal deep models.BMC bioinformatics · 2026Article
- Review
- Innovative diagnostic techniques and their clinical implications in food allergy: current clinical practice and future perspectives.Frontiers in allergy · 2026Review
- Early-life microbiome trajectories as biomarkers to predict health outcomes.Microbiome research reports · 2026Review
- Decoding the rhizosphere microbiome againstFrontiers in microbiomes · 2026Review
- Context-dependent roles of the gut microbiome in food allergy tolerance versus sensitization.Gut microbes · 2025Review
- A systematic longitudinal study of microbiome: integrating temporal-spatial dimensions with causal and deep learning models.BMC genomics · 2025Article
- MicroSTNet: a spatio-temporal graph-based framework for time-series microbiome analysis.Microbial genomics · 2025Article
- Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.bioRxiv : the preprint server for biology · 2025Article
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Prediction of pediatric peanut oral food challenge outcomes using machine learning.The journal of allergy and clinical immunology. Global · 2024Article
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- Can artificial intelligence (AI) replace oral food challenge?The Journal of allergy and clinical immunology · 2024Article
- Model-free prediction of microbiome compositions.Microbiome · 2024Article
- The future of food allergy diagnosis.Frontiers in allergy · 2024Review
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.