ArticleTheScientificWorldJournal2013
Identification of biomarkers for esophageal squamous cell carcinoma using feature selection and decision tree methods.
Article in TheScientificWorldJournal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 28 citations in OpenAlex.
- Machine learning-based model constructed from ultrasound radiomics and clinical features for predicting HER2 status in breast cancer patients with indeterminate (2+) immunohistochemical results.Cancer medicine · 2024Article
- A two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization.BMC bioinformatics · 2023Article
- Ensemble learning for predicting ex vivo human placental barrier permeability.BMC bioinformatics · 2022Article
- DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics.International journal of molecular sciences · 2021Article
- PD_BiBIM: Biclustering-based biomarker identification in ESCC microarray data.Journal of biosciences · 2021Article
- Epigenetic Alterations in Oesophageal Cancer: Expression and Role of the Involved Enzymes.International journal of molecular sciences · 2020Review
- Telomerase and Telomeres in Endometrial Cancer.Frontiers in oncology · 2019Review
- Discovery of Pyrazolo[4,3-Molecules (Basel, Switzerland) · 2018Article
- Exploration of gene functions for esophageal squamous cell carcinoma using network-based guilt by association principle.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2018Article
- Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach.Scientific reports · 2017Article
- Identification of informative features for predicting proinflammatory potentials of engine exhausts.Biomedical engineering online · 2017Article
- Discovery of Indeno[1,2-c]quinoline Derivatives as Potent Dual Antituberculosis and Anti-Inflammatory Agents.Molecules (Basel, Switzerland) · 2017Article
- Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens.Scientific reports · 2017Article
- The Role of Pontin and Reptin in Cellular Physiology and Cancer Etiology.Frontiers in molecular biosciences · 2017Review
- Article
- Downregulation of RUVBL1 inhibits proliferation of lung adenocarcinoma cells by G1/S phase cell cycle arrest via multiple mechanisms.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016Article
- Plasma matrix metalloproteinase 1 improves the detection and survival prediction of esophageal squamous cell carcinoma.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Esophageal squamous cell cancer (ESCC) is one of the most common fatal human cancers. The identification of biomarkers for early detection could be a promising strategy to decrease mortality. Previous studies utilized microarray techniques to identify more than one hundred genes; however, it is desirable to identify a small set of biomarkers for clinical use. This study proposes a sequential forward feature selection algorithm to design decision tree models for discriminating ESCC from normal tissues. Two potential biomarkers of RUVBL1 and CNIH were identified and validated based on two public available microarray datasets. To test the discrimination ability of the two biomarkers, 17 pairs of expression profiles of ESCC and normal tissues from Taiwanese male patients were measured by using microarray techniques. The classification accuracies of the two biomarkers in all three datasets were higher than 90%. Interpretable decision tree models were constructed to analyze expression patterns of the two biomarkers. RUVBL1 was consistently overexpressed in all three datasets, although we found inconsistent CNIH expression possibly affected by the diverse major risk factors for ESCC across different areas.
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