ArticleBioinformatics advances2023
A self-knowledge distillation-driven CNN-LSTM model for predicting disease outcomes using longitudinal microbiome data.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 20 citations in OpenAlex.
- Meta2DB: curated shotgun metagenomic feature sets and metadata for health state prediction.Bioinformatics (Oxford, England) · 2026Article
- Stable Longitudinal Screening of Latent Physiological Dysregulation from Psychometric Data Using Machine Learning.Bioengineering (Basel, Switzerland) · 2026Article
- An adaptive weight self-distillation deep learning framework for phenotype prediction from longitudinal gut microbiome data.BMC microbiology · 2026Article
- Review
- DynaBiome: interpretable unsupervised learning of gut microbiome dysbiosis via temporal deep models.BMC bioinformatics · 2026Article
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.iScience · 2025Review
- A systematic longitudinal study of microbiome: integrating temporal-spatial dimensions with causal and deep learning models.BMC genomics · 2025Article
- Nanomedicine-Driven Modulation of the Gut-Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer's and Parkinson's Disease.International journal of molecular sciences · 2025Review
- Diffusion model for imputing time-series gut microbiome profiles using phylogenetic information and metadata integration.Bioinformatics advances · 2025Article
- 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
- Exploring the frontier of microbiome biomarker discovery with artificial intelligence.National science review · 2024Article
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Human microbiome is complex and highly dynamic in nature. Dynamic patterns of the microbiome can capture more information than single point inference as it contains the temporal changes information. However, dynamic information of the human microbiome can be hard to be captured due to the complexity of obtaining the longitudinal data with a large volume of missing data that in conjunction with heterogeneity may provide a challenge for the data analysis. Results: We propose using an efficient hybrid deep learning architecture convolutional neural network-long short-term memory, which combines with self-knowledge distillation to create highly accurate models to analyze the longitudinal microbiome profiles to predict disease outcomes. Using our proposed models, we analyzed the datasets from Predicting Response to Standardized Pediatric Colitis Therapy (PROTECT) study and DIABIMMUNE study. We showed the significant improvement in the area under the receiver operating characteristic curve scores, achieving 0.889 and 0.798 on PROTECT study and DIABIMMUNE study, respectively, compared with state-of-the-art temporal deep learning models. Our findings provide an effective artificial intelligence-based tool to predict disease outcomes using longitudinal microbiome profiles from collected patients. Availability and implementation: The data and source code can be accessed at https://github.com/darylfung96/UC-disease-TL.
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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.