ArticleNature communications2021
Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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.
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Who cites it
8 citing papers in PubMed.
- Herbarium specimens in the age of artificial intelligence: from herbarium image identification to Integrative Taxonomic AI.Journal of plant research · 2026Article
- Connecting links with code: AI in morphological evolution.Frontiers in bioinformatics · 2026Review
- MS-UNet: A Hybrid Network with a Multi-Scale Vision Transformer and Attention Learning Confusion Regions for Soybean Rust Fungus.Sensors (Basel, Switzerland) · 2025Article
- Unraveling biochemical spatial patterns: Machine learning approaches to the inverse problem of stationary Turing patterns.iScience · 2024Article
- Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction.Patterns (New York, N.Y.) · 2024Article
- DCPlant phenomics (Washington, D.C.) · 2024Article
- Plant science in the age of simulation intelligence.Frontiers in plant science · 2023Article
- Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease.Nature communications · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is often limited to human-defined and static features. Here, we introduce a novel framework that can characterize shape changes (morphodynamics) for cell-drug interactions directly from images, and use it to interpret perturbed development of Phakopsora pachyrhizi, the Asian soybean rust crop pathogen. We describe population development over a 2D space of shapes (morphospace) using two models with condition-dependent parameters: a top-down Fokker-Planck model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. We discover a variety of landscapes, describing phenotype transitions during growth, and identify possible perturbations in the tip growth machinery that cause this variation. This demonstrates a widely-applicable integration of unsupervised learning and biophysical modeling.
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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.