ArticleJournal of experimental & clinical cancer research : CR2009
Lung cancer gene expression database analysis incorporating prior knowledge with support vector machine-based classification method.
Article in Journal of experimental & clinical cancer research : CR, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Construction of a 26‑feature gene support vector machine classifier for smoking and non‑smoking lung adenocarcinoma sample classification.Molecular medicine reports · 2018Pooled it
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
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- Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift.Bioengineering (Basel, Switzerland) · 2023Article
- Informed Attentive Predictors: A Generalisable Architecture for Prior Knowledge-Based Assisted Diagnosis of Cancers.Sensors (Basel, Switzerland) · 2021Article
- A Holistic Performance Comparison for Lung Cancer Classification Using Swarm Intelligence Techniques.Journal of healthcare engineering · 2021Article
- Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma.Scientific reports · 2019Article
- Lung cancer prediction from microarray data by gene expression programming.IET systems biology · 2016Article
- Identifying Significant Features in Cancer Methylation Data Using Gene Pathway Segmentation.Cancer informatics · 2016Article
- Triple-layer dissection of the lung adenocarcinoma transcriptome: regulation at the gene, transcript, and exon levels.Oncotarget · 2015Article
- Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation.PloS one · 2015Article
- Prediction of lung tumor types based on protein attributes by machine learning algorithms.SpringerPlus · 2013Article
- A novel method for finding non-small cell lung cancer diagnosis biomarkers.BMC medical genomics · 2013Article
- Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.PloS one · 2013Article
- Determination of SGK1 mRNA in non-small cell lung cancer samples underlines high expression in squamous cell carcinomas.Journal of experimental & clinical cancer research : CR · 2012Article
- Classification of lung cancer tumors based on structural and physicochemical properties of proteins by bioinformatics models.PloS one · 2012Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundA reliable and precise classification is essential for successful diagnosis and treatment of cancer. Gene expression microarrays have provided the high-throughput platform to discover genomic biomarkers for cancer diagnosis and prognosis. Rational use of the available bioinformation can not only effectively remove or suppress noise in gene chips, but also avoid one-sided results of separate experiment. However, only some studies have been aware of the importance of prior information in cancer classification.
methodsTogether with the application of support vector machine as the discriminant approach, we proposed one modified method that incorporated prior knowledge into cancer classification based on gene expression data to improve accuracy. A public well-known dataset, Malignant pleural mesothelioma and lung adenocarcinoma gene expression database, was used in this study. Prior knowledge is viewed here as a means of directing the classifier using known lung adenocarcinoma related genes. The procedures were performed by software R 2.80.
resultsThe modified method performed better after incorporating prior knowledge. Accuracy of the modified method improved from 98.86% to 100% in training set and from 98.51% to 99.06% in test set. The standard deviations of the modified method decreased from 0.26% to 0 in training set and from 3.04% to 2.10% in test set.
conclusionThe method that incorporates prior knowledge into discriminant analysis could effectively improve the capacity and reduce the impact of noise. This idea may have good future not only in practice but also in methodology.
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