Efficient feature selection on gene expression data: Which algorithm to use?
| Autor: | Michail Tsagris, Kleanthi Lakiotaki, Ioannis Tsamardinos, Zacharias Papadovasilakis |
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| Rok vydání: | 2018 |
| Předmět: |
0303 health sciences
Computer science Regression analysis Statistical model Feature selection 01 natural sciences 010104 statistics & probability 03 medical and health sciences Variable (computer science) Lasso (statistics) Feature (machine learning) 0101 mathematics Algorithm Selection (genetic algorithm) 030304 developmental biology |
| DOI: | 10.1101/431734 |
| Popis: | BackgroundFeature selection seeks to identify a minimal-size subset of features that is maximally predictive of the outcome of interest. It is particularly important for biomarker discovery from high-dimensional molecular data, where the features could correspond to gene expressions, Single Nucleotide Polymorphisms (SNPs), proteins concentrations, e.t.c. We evaluate, empirically, three state-of-the-art, feature selection algorithms, scalable to high-dimensional data: a novel generalized variant of OMP (gOMP), LASSO and FBED. All three greedily select the next feature to include; the first two employ the residuals re-sulting from the current selection, while the latter rebuilds a statistical model. The algorithms are compared in terms of predictive performance, number of selected features and computational efficiency, on gene expression data with either survival time (censored time-to-event) or disease status (case-control) as an outcome. This work attempts to answer a) whether gOMP is to be preferred over LASSO and b) whether residual-based algorithms, e.g. gOMP, are to be preferred over algorithms, such as FBED, that rely heavily on regression model fitting.ResultsgOMP is on par, or outperforms LASSO in all metrics, predictive performance, number of features selected and computational efficiency. Contrasting gOMP to FBED, both exhibit similar performance in terms of predictive performance and number of selected features. Overall, gOMP combines the benefits of both LASSO and FBED; it is computationally efficient and produces parsimonious models of high predictive performance.ConclusionsThe use of gOMP is suggested for variable selection with high-dimensional gene expression data, and the target variable need not be restricted to time-to-event or case control, as examined in this paper. |
| Databáze: | OpenAIRE |
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