ArticleBMC medical informatics and decision making2018
A longitudinal feature selection method identifies relevant genes to distinguish complicated injury and uncomplicated injury over time.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- An ensemble of the iCluster method to analyze longitudinal lncRNA expression data for psoriasis patients.Human genomics · 2021Article
- GEE-TGDR: A Longitudinal Feature Selection Algorithm and Its Application to lncRNA Expression Profiles for Psoriasis Patients Treated with Immune Therapies.BioMed research international · 2021Article
- Evaluation of Feature Selection Methods for Mammographic Breast Cancer Diagnosis in a Unified Framework.BioMed research international · 2021Article
- Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures.BioMed research international · 2019Review
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3 authors.
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Abstract
backgroundFeature selection and gene set analysis are of increasing interest in the field of bioinformatics. While these two approaches have been developed for different purposes, we describe how some gene set analysis methods can be utilized to conduct feature selection.
methodsWe adopted a gene set analysis method, the significance analysis of microarray gene set reduction (SAMGSR) algorithm, to carry out feature selection for longitudinal gene expression data.
resultsUsing a real-world application and simulated data, it is demonstrated that the proposed SAMGSR extension outperforms other relevant methods. In this study, we illustrate that a gene's expression profiles over time can be regarded as a gene set and then a suitable gene set analysis method can be utilized directly to select relevant genes associated with the phenotype of interest over time.
conclusionsWe believe this work will motivate more research to bridge feature selection and gene set analysis, with the development of novel algorithms capable of carrying out feature selection for longitudinal gene expression data.
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