ArticleNature machine intelligence2022
Hierarchical deep reinforcement learning reveals novel mechanism of cell movement.
Article in Nature machine intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Review
- EmbSAM: cell boundary localization and Segment Anything Model for fast images of developing embryos.Communications biology · 2025Article
- Discovery of the reward function for embodied reinforcement learning agents.Nature communications · 2025Article
- Multi-objective optimization for dynamic logistics scheduling based on hierarchical deep reinforcement learning.Scientific reports · 2025Article
- An effective method for quantification, visualization, and analysis of 3D cell shape during early embryogenesis.Quantitative biology (Beijing, China) · 2025Article
- The paradigm shift in neural stem cells basic research driven by artificial intelligence related technologies.Frontiers in cellular neuroscience · 2025Review
- Challenges and opportunities in bioimage analysis.Nature methods · 2023Article
- A compound PCP scheme underlies sequential rosettes-based cell intercalation.Development (Cambridge, England) · 2023Article
- Delineating the mechanisms and design principles ofComputational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Time-lapse images of cells and tissues contain rich information of dynamic cell behaviors, which reflect the underlying processes of proliferation, differentiation and morphogenesis. However, we lack computational tools for effective inference. Here, we exploit Deep Reinforcement Learning (DRL) to infer cell-cell interactions and collective cell behaviors in tissue morphogenesis from 3D, time-lapse images. We used Hierarchical DRL (HDRL), known for multiscale learning and data efficiency, to examine cell migrations based on images with ubiquitous nuclear label and simple rules formulated from empirical statistics of the images. When applied to
Identifiers
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.