ArticleScientific reports2023
Application of self-organizing maps to AFM-based viscoelastic characterization of breast cancer cell mechanics.
Article in Scientific reports, 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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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
16 citing papers in PubMed, 26 citations in OpenAlex.
- Understanding and targeting the tumour matrisome.Nature reviews. Clinical oncology · 2026Review
- Single Cell Mechanics in Disease Progression.Small science · 2026Review
- Nanoscale Analysis beyond Imaging by Atomic Force Microscopy: Molecular Perspectives on Oncology and Neurodegeneration.Small science · 2025Review
- Advances in nanomechanical property mapping by atomic force microscopy.Nanoscale advances · 2025Review
- High-Throughput Nanorheology of Living Cells Powered by Supervised Machine Learning.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2025Article
- Uncovering subtype-specific metabolic signatures in breast cancer through multimodal integration, attention-based deep learning, and self-organizing maps.Scientific reports · 2025Article
- The Mechanical Properties of Breast Cancer Cells and Their Surrounding Microenvironment.International journal of molecular sciences · 2025Review
- Understanding generational differences in digital skills and recreational behaviour for effective visitor management in forest destinations.Scientific reports · 2025Article
- Nanomechanical characterization of soft nanomaterial using atomic force microscopy.Materials today. Bio · 2025Review
- Data-driven analysis for the evaluation of cortical mechanics of non-adherent cells.Scientific reports · 2025Article
- Article
- Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis.Computational and structural biotechnology journal · 2024Review
- Correlative Raman Imaging: Development and Cancer Applications.Biosensors · 2024Review
- Atomic Force Microscopy for the Study of Cell Mechanics in Pharmaceutics.Pharmaceutics · 2024Review
- Machine learning-based approach for automated classification of cell and extracellular matrix using nanomechanical properties.Materials today. Bio · 2024Article
- E-cadherin adhesion dynamics as revealed by an accelerated force ramp are dependent upon the presence of α-catenin.Biochemical and biophysical research communications · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Cell mechanical properties have been proposed as label free markers for diagnostic purposes in diseases such as cancer. Cancer cells show altered mechanical phenotypes compared to their healthy counterparts. Atomic Force Microscopy (AFM) is a widely utilized tool to study cell mechanics. These measurements often need skilful users, physical modelling of mechanical properties and expertise in data interpretation. Together with the need to perform many measurements for statistical significance and to probe wide enough areas in tissue structures, the application of machine learning and artificial neural network techniques to automatically classify AFM datasets has received interest recently. We propose the use of self-organizing maps (SOMs) as unsupervised artificial neural network applied to mechanical measurements performed via AFM on epithelial breast cancer cells treated with different substances that affect estrogen receptor signalling. We show changes in mechanical properties due to treatments, as estrogen softened the cells, while resveratrol led to an increase in cell stiffness and viscosity. These data were then used as input for SOMs. Our approach was able to distinguish between estrogen treated, control and resveratrol treated cells in an unsupervised manner. In addition, the maps enabled investigation of the relationship of the input variables.
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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.