ArticleHealthcare (Basel, Switzerland)2021
Modeling and Predicting the Cell Migration Properties from Scratch Wound Healing Assay on Cisplatin-Resistant Ovarian Cancer Cell Lines Using Artificial Neural Network.
Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Hydrazones as Potential Antimelanoma Prototypes: A Systematic and Patent Review.Anti-cancer agents in medicinal chemistry · 2026Pooled it
- Wound Healing Assay.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Electrospun PVA Nanofibers Co-Loaded with Atorvastatin and Zinc Oxide for Antibacterial and In Vitro Wound Healing Applications.Biomedicines · 2026Article
- WoundPy: an improved automatized method for in vitro cell motility investigation based on wound alignment and absolute velocity profiling.Frontiers in cell and developmental biology · 2026Article
- From Screening to a Nanotechnological Platform: Cannabidiol-Chemotherapy Co-Loaded Lipid Nanocapsules for Glioblastoma Multiforme Treatment.Pharmaceutics · 2025Article
- Assessment of the toxic effect of benzalkonium chloride on human limbal stem cells.Scientific reports · 2025Article
- Sera from Rheumatoid Arthritis Patients Induce Oxidative Stress and Pro-Angiogenic and Profibrotic Phenotypes in Human Endothelial Cells.Journal of clinical medicine · 2024Article
- Epigenetic silencing of ZIC4 unveils a potential tumor suppressor role in pediatric choroid plexus carcinoma.Scientific reports · 2024Article
- Article
- Efficacy of metformin and electrical pulses in breast cancer MDA-MB-231 cells.Exploration of targeted anti-tumor therapy · 2024Article
- Validating Wound Severity Assessment via Region-Anchored Convolutional Neural Network Model for Mobile Image-Based Size and Tissue Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Hypophyllanthin and Phyllanthin fromACS omega · 2023Article
- miR‑424‑5p is downregulated in the placentas of patients with preeclampsia and affects trophoblast migration and invasion.Experimental and therapeutic medicine · 2023Article
- Development and verification of a combined diagnostic model for primary Sjögren's syndrome by integrated bioinformatics analysis and machine learning.Scientific reports · 2023Article
- Increased Expression of the RBPMS Splice Variants Inhibits Cell Proliferation in Ovarian Cancer Cells.International journal of molecular sciences · 2022Article
- High Throughput 3D Cell Migration Assay Using Micropillar/Microwell Chips.Molecules (Basel, Switzerland) · 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
2 authors at 1 institution in 1 country.
Funding
Abstract
The study of artificial neural networks (ANN) has undergone a tremendous revolution in recent years, boosted by deep learning tools. The presence of a greater number of learning tools and their applications, in particular, favors this revolution. However, there is a significant need to deal with the issue of implementing a systematic method during the development phase of the ANN to increase its performance. A multilayer feedforward neural network (FNN) was proposed in this paper to predict the cell migration assay on cisplatin-sensitive and cisplatin-resistant (CisR) ovarian cancer (OC) cell lines via scratch wound healing assay. An FNN training algorithm model was generated using the MATLAB fitting function in a MATLAB script to accomplish this task. The input parameters were types of cell lines, times, and wound area, and outputs were relative wound area, percentage of wound closure, and wound healing speed. In addition, we tested and compared the initial accuracy of various supervised learning classifier and support vector regression (SVR) algorithms. The proposed ANN model achieved good agreement with the experimental data and minimized error between the estimated and experimental values. The conclusions drawn demonstrate that the developed ANN model is a useful, accurate, fast, and inexpensive method to predict cancerous cell migration characteristics evaluated via scratch wound healing assay.
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.