ArticleComputational and structural biotechnology journal2020
Deep learning predicts microbial interactions from self-organized spatiotemporal patterns.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 40 citations in OpenAlex.
- Image-based high-dimensional deep learning framework for spatiotemporal modeling, prediction and transfer learning of microbial cultivation processes.Bioprocess and biosystems engineering · 2026Article
- Niche Construction: A Rational Engineering Strategy for Regulating Microbial Assembly to Enhance Flavor and Bioactivity of Fermented Foods.Comprehensive reviews in food science and food safety · 2026Review
- Probiotics and Postbiotics in Life-Style Disease Management: A Comprehensive Review on the Technologies in the Era of Omics and Artificial Intelligence.Probiotics and antimicrobial proteins · 2026Review
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Navigating complexity: key considerations for studying fungal-bacterial interactions.mSystems · 2026Article
- Artificial Intelligence for Microbial Isolation and Cultivation: Progress and Challenges.Microorganisms · 2026Review
- Spatial structure: shaping the ecology and evolution of microbial communities.FEMS microbiology reviews · 2026Review
- Knowledge graph embedding for predicting and analyzing microbial interactions.Scientific reports · 2025Article
- Living Kombucha Electronics with Proteinoids.ACS omega · 2025Article
- Advances in synthetic microbial ecosystems approach for studying ecological interactions and their influencing factors.Engineering microbiology · 2025Review
- Disentangling the feedback loops driving spatial patterning in microbial communities.NPJ biofilms and microbiomes · 2025Review
- Deciphering and designing microbial communities by genome-scale metabolic modelling.Computational and structural biotechnology journal · 2024Review
- SPLANG-a synthetic poisson-lognormal-based abundance and network generative model for microbial interaction inference algorithms.Scientific reports · 2024Article
- Microbial community composition predicts bacterial production across ocean ecosystems.The ISME journal · 2024Article
- Calibrating spatiotemporal models of microbial communities to microscopy data: A review.PLoS computational biology · 2022Review
- Synthetic Soil Aggregates: Bioprinted Habitats for High-Throughput Microbial Metaphenomics.Microorganisms · 2022Article
- Machine Learning Predicts Biogeochemistry from Microbial Community Structure in a Complex Model System.Microbiology spectrum · 2022Article
- Increasing a microscope's effective field of view via overlapped imaging and machine learning.Optics express · 2022Article
- Machine Learning Advances in Microbiology: A Review of Methods and Applications.Frontiers in microbiology · 2022Review
- Seeing the bigger picture.Nature reviews. Microbiology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microbial communities organize into spatial patterns that are largely governed by interspecies interactions. This phenomenon is an important metric for understanding community functional dynamics, yet the use of spatial patterns for predicting microbial interactions is currently lacking. Here we propose supervised deep learning as a new tool for network inference. An agent-based model was used to simulate the spatiotemporal evolution of two interacting organisms under diverse growth and interaction scenarios, the data of which was subsequently used to train deep neural networks. For small-size domains (100 µm × 100 µm) over which interaction coefficients are assumed to be invariant, we obtained fairly accurate predictions, as indicated by an average R
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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.