ArticleScientific reports2020
Towards image-based cancer cell lines authentication using deep neural networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Development of a quality control panel based on patient-derived immortalized B-Lymphoblastoid cell lines for thalassemia genotyping.Molecular genetics and metabolism reports · 2026Article
- Elderly Patients with Tuberculosis Combined with Diabetes Mellitus: A Comprehensive Analysis of Lymphocyte Subpopulation Dynamics, Clinical Features, Drug Resistance and Disease Regression.International journal of general medicine · 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
- Single-cell morphological and transcriptome analysis unveil inhibitors of polyploid giant breast cancer cells in vitro.Communications biology · 2023Article
- Improving segmentation and classification of renal tumors in small sample 3D CT images using transfer learning with convolutional neural networks.International journal of computer assisted radiology and surgery · 2022Article
- Establishing Institutional Scores With the Rigor and Transparency Index: Large-scale Analysis of Scientific Reporting Quality.Journal of medical Internet research · 2022Article
- An automated cell line authentication method for AstraZeneca global cell bank using deep neural networks on brightfield images.Scientific reports · 2022Article
- A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines (cell lines of the same genetic origin, e.g. different cell lines derived from the same organism, clonal sublines, sublines adapted to grow under certain conditions). Hence, additional complementary, ideally low-cost and low-effort methods are required that enable (1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line (COLO 704, EFO-21, EFO-27 and UKF-NB-3) and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704
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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.