ArticleBiomarkers in medicine2019
Pancreatic cancer biomarker detection by two support vector strategies for recursive feature elimination.
Article in Biomarkers in medicine, 2019. 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, 17 citations in OpenAlex.
- The diagnostic and prognostic value ofBioImpacts : BI · 2025Article
- Article
- The Utility of Artificial Intelligence in the Diagnosis and Management of Pancreatic Cancer.Cureus · 2023Review
- Review
- Artificial intelligence in pancreatic cancer.Theranostics · 2022Review
- Identification of sub-Golgi protein localization by use of deep representation learning features.Bioinformatics (Oxford, England) · 2021Article
- DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins.Frontiers in pharmacology · 2021Review
- Liver-Metastasis-Related Genes are Potential Biomarkers for Predicting the Clinical Outcomes of Patients with Pancreatic Adenocarcinoma.Pathology oncology research : POR · 2021Article
- PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron.Computational and mathematical methods in medicine · 2020Article
- Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
Abstract
aimPancreatic cancer is one of the worst malignant tumors in prognosis. Therefore, to reduce the mortality rate of pancreatic cancer, early diagnosis and prompt treatment are particularly important.
resultsWe put forward a new feature-selection method that was used to find clinical markers for pancreatic cancer by combination of Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Large Margin Distribution Machine Recursive Feature Elimination (LDM-RFE) algorithms. As a result, seven differentially expressed genes were predicted as specific biomarkers for pancreatic cancer because of their highest accuracy of classification on cancer and normal samples.
conclusionThree (MMP7, FOS and A2M) out of the seven predicted gene markers were found to encode proteins secreted into urine, providing potential diagnostic evidences for pancreatic cancer.
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.