ArticleNature communications2023
Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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.
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Who cites it
16 citing papers in PubMed.
- Engineering microbial consortia for distributed signal processing.Nature communications · 2026Article
- Use of deep learning to predict chronic wasting disease status based on animal movement.Movement ecology · 2026Article
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Acentric artificial intelligence with deep feature engineering for early heart disease risk prediction.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.iScience · 2025Review
- A foundation model for microbial growth dynamics.bioRxiv : the preprint server for biology · 2025Article
- Review
- Smart Microbiomes: How AI Is Revolutionizing Personalized Medicine.Bioengineering (Basel, Switzerland) · 2025Review
- Linear scaling reveals low-dimensional structure in observable microbial dynamics.bioRxiv : the preprint server for biology · 2025Article
- Dynamics of an algae-bacteria microcosm: Photosynthesis, chemotaxis, and expulsion in inhomogeneous active matter.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The human microbiome in clinical translation: from bench to bedside.Frontiers in microbiology · 2025Review
- Data-driven discovery of the interplay between genetic and environmental factors in bacterial growth.Communications biology · 2024Article
- Linking spatial drug heterogeneity to microbial growth dynamics in theory and experiment.bioRxiv : the preprint server for biology · 2024Article
- Private benefit of β-lactamase dictates selection dynamics of combination antibiotic treatment.Nature communications · 2024Article
- Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity. These low-dimensional embeddings are just as effective, if not better, than raw data for tasks such as identifying bacterial strains, predicting traits like antibiotic resistance, and predicting community dynamics. Additionally, we demonstrate that essential dynamical information of these systems can be captured using far fewer variables than traditional mechanistic models. Our work suggests that machine learning can enable the creation of concise representations of high-dimensional microbiome dynamics to facilitate data analysis and gain new biological insights.
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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.