ArticleAnalytical chemistry2020
Early Prediction of Single-Cell Derived Sphere Formation Rate Using Convolutional Neural Network Image Analysis.
Article in Analytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- High-Throughput Empirical and Virtual Screening To Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer.Analytical chemistry · 2025Article
- High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer.bioRxiv : the preprint server for biology · 2024Article
- Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.Lab on a chip · 2024Article
- Microfluidics-based patient-derived disease detection tool for deep learning-assisted precision medicine.Biomicrofluidics · 2024Article
- Single-cell morphological and transcriptome analysis unveil inhibitors of polyploid giant breast cancer cells in vitro.Communications biology · 2023Article
- High-Throughput Cellular Heterogeneity Analysis in Cell Migration at the Single-Cell Level.Small (Weinheim an der Bergstrasse, Germany) · 2023Article
- Computer vision meets microfluidics: a label-free method for high-throughput cell analysis.Microsystems & nanoengineering · 2023Review
- Machine learning-based detection of label-free cancer stem-like cell fate.Scientific reports · 2022Article
- Early Predictor Tool of Disease Using Label-Free Liquid Biopsy-Based Platforms for Patient-Centric Healthcare.Cancers · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Functional identification of cancer stem-like cells (CSCs) is an established method to identify and study this cancer subpopulation critical for cancer progression and metastasis. The method is based on the unique capability of single CSCs to survive and grow to tumorspheres in harsh suspension culture environment. Recent advances in microfluidic technology have enabled isolating and culturing thousands of single cells on a chip. However, tumorsphere assay takes a relatively long period of time, limiting the throughput of this assay. In this work, we incorporated machine learning with single-cell analysis to expedite tumorsphere assay. We collected 1,710 single-cell events as the database and trained a convolutional neural network model that predicts whether a single cell could grow to a tumorsphere on Day 14 based on its Day 4 image. With this future-telling model, we precisely estimated the sphere formation rate of SUM159 breast cancer cells to be 17.8% based on Day 4 images. The estimation was close to the ground truth of 17.6% on Day 14. The preliminary work demonstrates not only the feasibility to significantly accelerate tumorsphere assay but also a synergistic combination between single-cell analysis with machine learning, which can be applied to many other biomedical applications.
Indexed as
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.