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GEE-TGDR: A longitudinal feature selection algorithm and its application to lncRNA expression profiles for psoriasis patients treated with immune therapies
- Source :
- BioMed Research International, Vol 2021 (2021), BioMed Research International
- Publication Year :
- 2020
-
Abstract
- With the fast evolution of high-throughput technology, longitudinal gene expression experiments have become affordable and increasingly common in biomedical fields. Generalized estimating equation (GEE) approach is a widely used statistical method for the analysis of longitudinal data. Feature selection is imperative in longitudinal omics data analysis. Among a variety of existing feature selection methods, an embedded method—threshold gradient descent regularization (TGDR)—stands out due to its excellent characteristics. An alignment of GEE with TGDR is a promising area for the purpose of identifying relevant markers that can explain the dynamic changes of outcomes across time. We proposed a new novel feature selection algorithm for longitudinal outcomes—GEE-TGDR. In the GEE-TGDR method, the corresponding quasilikelihood function of a GEE model is the objective function to be optimized, and the optimization and feature selection are accomplished by the TGDR method. Long noncoding RNAs (lncRNAs) are posttranscriptional and epigenetic regulators and have lower expression levels and are more tissue-specific compared with protein-coding genes. So far, the implication of lncRNAs in psoriasis remains largely unexplored and poorly understood even though some evidence in the literature supports that lncRNAs and psoriasis are highly associated. In this study, we applied the GEE-TGDR method to a lncRNA expression dataset that examined the response of psoriasis patients to immune treatments. As a result, a list including 10 relevant lncRNAs was identified with a predictive accuracy of 70% that is superior to the accuracies achieved by two competitive methods and meaningful biological interpretation. A widespread application of the GEE-TGDR method in omics longitudinal data analysis is anticipated.
- Subjects :
- FOS: Computer and information sciences
0301 basic medicine
Article Subject
Computer science
Gene regulatory network
Feature selection
01 natural sciences
Statistics - Applications
General Biochemistry, Genetics and Molecular Biology
Gee
010104 statistics & probability
03 medical and health sciences
Psoriasis
medicine
Humans
Applications (stat.AP)
Gene Regulatory Networks
RNA, Messenger
0101 mathematics
Generalized estimating equation
General Immunology and Microbiology
General Medicine
medicine.disease
Expression (mathematics)
030104 developmental biology
Gene Expression Regulation
Medicine
RNA, Long Noncoding
Immunotherapy
Gradient descent
Algorithm
Function (biology)
Algorithms
Research Article
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- BioMed Research International, Vol 2021 (2021), BioMed Research International
- Accession number :
- edsair.doi.dedup.....b6ffb6eb197aeacdfe652d8ed94e4a38