ArticleBiology direct2016
Weighted-SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes.
Article in Biology direct, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data.Entropy (Basel, Switzerland) · 2021Article
- Weighted gene expression profiles identify diagnostic and prognostic genes for lung adenocarcinoma and squamous cell carcinoma.The Journal of international medical research · 2020Article
- Identification of Monotonically Differentially Expressed Genes across Pathologic Stages for Cancers.Journal of oncology · 2020Article
- Identification of monotonically differentially expressed genes for non-small cell lung cancer.BMC bioinformatics · 2019Article
- Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time.BioMed research international · 2019Article
- Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures.BioMed research international · 2019Review
- Identification of Monotonically Differentially Expressed Genes for IFN-BioMed research international · 2019Article
- A longitudinal feature selection method identifies relevant genes to distinguish complicated injury and uncomplicated injury over time.BMC medical informatics and decision making · 2018Article
- Identification of subtype-specific prognostic signatures using Cox models with redundant gene elimination.Oncology letters · 2018Article
- To select relevant features for longitudinal gene expression data by extending a pathway analysis method.F1000Research · 2018Article
- Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients.Oncology letters · 2017Article
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3 authors.
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Abstract
backgroundIt has been demonstrated that a pathway-based feature selection method that incorporates biological information within pathways during the process of feature selection usually outperforms a gene-based feature selection algorithm in terms of predictive accuracy and stability. Significance analysis of microarray-gene set reduction algorithm (SAMGSR), an extension to a gene set analysis method with further reduction of the selected pathways to their respective core subsets, can be regarded as a pathway-based feature selection method.
methodsIn SAMGSR, whether a gene is selected is mainly determined by its expression difference between the phenotypes, and partially by the number of pathways to which this gene belongs. It ignores the topology information among pathways. In this study, we propose a weighted version of the SAMGSR algorithm by constructing weights based on the connectivity among genes and then combing these weights with the test statistics.
resultsUsing both simulated and real-world data, we evaluate the performance of the proposed SAMGSR extension and demonstrate that the weighted version outperforms its original version.
conclusionsTo conclude, the additional gene connectivity information does faciliatate feature selection. REVIEWERS: This article was reviewed by Drs. Limsoon Wong, Lev Klebanov, and, I. King Jordan.
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