ArticleF1000Research2018
To select relevant features for longitudinal gene expression data by extending a pathway analysis method.
Article in F1000Research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- An ensemble of the iCluster method to analyze longitudinal lncRNA expression data for psoriasis patients.Human genomics · 2021Article
- 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
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Authors and funding
3 authors.
Funding
Abstract
The emerging field of pathway-based feature selection that incorporates biological information conveyed by gene sets/pathways to guide the selection of relevant genes has become increasingly popular and widespread. In this study, we adapt a gene set analysis method - the significance analysis of microarray gene set reduction (SAMGSR) algorithm to carry out feature selection for longitudinal microarray data, and propose a pathway-based feature selection algorithm - the two-level SAMGSR method. By using simulated data and a real-world application, we demonstrate that a gene's expression profiles over time can be considered as a gene set. Thus a suitable gene set analysis method can be utilized or modified to execute the selection of relevant genes for longitudinal omics data. We believe this work paves the way for more research to bridge feature selection and gene set analysis with the development of novel pathway-based feature selection algorithms.
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Registered trials
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