Tilting the lasso by knowledge-based post-processing

Tharmaratnam, Kukatharmini and Sperrin, Matthew and Jaki, Thomas Friedrich and Reppe, Sjur and Frigessi, Arnoldo (2016) Tilting the lasso by knowledge-based post-processing. BMC Bioinformatics, 17. ISSN 1471-2105

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Background It is useful to incorporate biological knowledge on the role of genetic determinants in predicting an outcome. It is, however, not always feasible to fully elicit this information when the number of determinants is large. We present an approach to overcome this difficulty. First, using half of the available data, a shortlist of potentially interesting determinants are generated. Second, binary indications of biological importance are elicited for this much smaller number of determinants. Third, an analysis is carried out on this shortlist using the second half of the data. Results We show through simulations that, compared with adaptive lasso, this approach leads to models containing more biologically relevant variables, while the prediction mean squared error (PMSE) is comparable or even reduced. We also apply our approach to bone mineral density data, and again final models contain more biologically relevant variables and have reduced PMSEs. Conclusion Our method leads to comparable or improved predictive performance, and models with greater face validity and interpretability with feasible incorporation of biological knowledge into predictive models.

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Journal Article
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BMC Bioinformatics
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14 Sep 2016 13:08
Last Modified:
22 Sep 2023 00:29