ArticleCancers2019
Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model.
Article in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Review
- Machine learning reveals glycolytic key gene in gastric cancer prognosis.Scientific reports · 2025Article
- Enhancing Personalized Chemotherapy for Ovarian Cancer: Integrating Gene Expression Data with Machine Learning.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Prediction of ovarian cancer using artificial intelligence tools.Health science reports · 2024Article
- Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning.BioMedInformatics · 2024Article
- Bioinformatics in Early Cancer Detection.Cureus · 2023Review
- A Subset of Secreted Proteins in Ascites Can Predict Platinum-Free Interval in Ovarian Cancer.Cancers · 2022Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Clinical significance of metabolism-related genes and FAK activity in ovarian high-grade serous carcinoma.BMC cancer · 2022Article
- Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.Current genomics · 2021Review
- Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures.NPJ genomic medicine · 2021Article
- Article
- Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology.Frontiers in pharmacology · 2020Review
- Article
- Applications of Bioinformatics in Cancer.Cancers · 2019Article
- Article
- Supervised Machine Learning Predictive Analytics For Triple-Negative Breast Cancer Death Outcomes.OncoTargets and therapy · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Epithelial ovarian cancer patients usually relapse after primary management. We utilized the support vector machine algorithm to develop a model for the chemo-response using the Cancer Cell Line Encyclopedia (CCLE) and validated the model in The Cancer Genome Atlas (TCGA) and the GSE9891 dataset. Finally, we evaluated the feasibility of the model using ovarian cancer patients from our institute. The 10-gene predictive model demonstrated that the high response group had a longer recurrence-free survival (RFS) (log-rank test,
Indexed as
Identifiers
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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.