ArticleBiomaterials2016
Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues.
Article in Biomaterials, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- High-Throughput Digital Decoding of Vascular Heterogeneity in Patient-Specific Tumor Microenvironments.Advanced healthcare materials · 2026Article
- A Microwell Platform for Characterizing the Dynamic Response of Corneal Keratocytes to Biochemical and Biophysical Cues.Micromachines · 2026Article
- Quantitative analysis of fibroblast migration reveals migratory states characterized by force generation, cell shape and motion.bioRxiv : the preprint server for biology · 2026Article
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Artificial intelligence in orthopedic regenerative medicine: from design to clinical translational pathways.Frontiers in cell and developmental biology · 2026Review
- Current Trends and Future Opportunities of AI-Based Analysis in Mesenchymal Stem Cell Imaging: A Scoping Review.Journal of imaging · 2025Review
- Representation of high-dimensional cell morphology and morphodynamics in 2D latent space.Physical biology · 2025Article
- Evaluation of image analysis tools for the measurement of cellular morphology.Frontiers in cell and developmental biology · 2025Review
- Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Review
- CellVisioner: A Generalizable Cell Virtual Staining Toolbox based on Few-Shot Transfer Learning for Mechanobiological Analysis.Research (Washington, D.C.) · 2023Article
- Morphology-based deep learning approach for predicting adipogenic and osteogenic differentiation of human mesenchymal stem cells (hMSCs).Frontiers in cell and developmental biology · 2023Article
- Machine learning and 3D bioprinting.International journal of bioprinting · 2023Article
- High throughput screening of mesenchymal stem cell lines using deep learning.Scientific reports · 2022Article
- TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images.Cancers · 2022Article
- Decomposition of cell activities revealing the role of the cell cycle in driving biofunctional heterogeneity.Scientific reports · 2021Article
- Article
- High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology.Chemical reviews · 2021Review
- Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks.Scientific reports · 2021Article
- Morphology-Based Deep Learning Approach for Predicting Osteogenic Differentiation.Frontiers in bioengineering and biotechnology · 2021Article
- The LTB4-BLT1 axis regulates actomyosin and β2-integrin dynamics during neutrophil extravasation.The Journal of cell biology · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Cell morphology has been identified as a potential indicator of stem cell response to biomaterials. However, determination of cell shape phenotype in biomaterials is complicated by heterogeneous cell populations, microenvironment heterogeneity, and multi-parametric definitions of cell morphology. To associate cell morphology with cell-material interactions, we developed a shape phenotyping framework based on support vector machines. A feature selection procedure was implemented to select the most significant combination of cell shape metrics to build classifiers with both accuracy and stability to identify and predict microenvironment-driven morphological differences in heterogeneous cell populations. The analysis was conducted at a multi-cell level, where a "supercell" method used average shape measurements of small groups of single cells to account for heterogeneous populations and microenvironment. A subsampling validation algorithm revealed the range of supercell sizes and sample sizes needed for classifier stability and generalization capability. As an example, the responses of human bone marrow stromal cells (hBMSCs) to fibrous vs flat microenvironments were compared on day 1. Our analysis showed that 57 cells (grouped into supercells of size 4) are the minimum needed for phenotyping. The analysis identified that a combination of minor axis length, solidity, and mean negative curvature were the strongest early shape-based indicator of hBMSCs response to fibrous microenvironment.
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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.