ArticleNPJ systems biology and applications2021
Cell morphology-based machine learning models for human cell state classification.
Article in NPJ systems biology and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- A new era in CAR-NK cell therapy: from technological innovations to clinical applications.World journal of pediatrics : WJP · 2026Review
- Non-invasive quantification of viability in liver spheroids using deep learning.Frontiers in bioengineering and biotechnology · 2026Article
- Unstained Blood Smear Analysis: A Review of Rule-Based, Machine Learning, and Deep Learning Techniques.Journal of biophotonics · 2025Review
- Enhanced detection of malaria infected red blood cells through phase driven classification.Scientific reports · 2025Article
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- Perfluorooctanoic Acid and Its Short-Chain Substitutes Induce Cytotoxic and Prooxidative Changes in Human Peripheral Blood Mononuclear Cells: A Comparative Study.International journal of molecular sciences · 2025Article
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage.Journal of neural engineering · 2024Article
- Comprehensive in vitro and in ovo assessment of cytotoxicity: Unraveling the impact of sodium fluoride, xylitol, and their synergistic associations in dental products.Biomolecules & biomedicine · 2024Article
- Spatial Morphoproteomic Features Predict Uniqueness of Immune Microarchitectures and Responses in Lymphoid Follicles.bioRxiv : the preprint server for biology · 2024Article
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- Stem cell-based approaches for developmental neurotoxicity testing.Frontiers in toxicology · 2024Review
- Measuring Drug Response with Single-Cell Growth Rate Quantification.Analytical chemistry · 2023Article
- NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage.bioRxiv : the preprint server for biology · 2023Article
- Machine learning implementation strategy in imaging and impedance flow cytometry.Biomicrofluidics · 2023Review
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- Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning.Sensors (Basel, Switzerland) · 2023Review
- Diagnosis of autism spectrum disorder based on functional brain networks and machine learning.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 live f value and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information.
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