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seagull: lasso, group lasso and sparse-group lasso regularisation for linear regression models via proximal gradient descent

Published on Feb 14, 2020in bioRxiv
· DOI :10.1101/2020.02.13.947473
Jan Klosa1
Estimated H-index: 1
(Leibniz Association),
Noah Simon14
Estimated H-index: 14
(UW: University of Washington)
+ 2 AuthorsDörte Wittenburg7
Estimated H-index: 7
(Leibniz Association)
Abstract
Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalisation approaches are often the methods of choice. They are especially useful in case of multicollinearity which appears if the number of explanatory variables exceeds the number of ob-servations or for some biological reason. Then, the model goodness of fit is penalised by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realised by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally, the package produces complete regularisation paths.
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References7
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Summary The DNA methylation levels of certain CpG sites are thought to reflect the pace of human aging. Here, we developed a robust predictor of mouse biological age based on 90 CpG sites derived from partial blood DNA methylation profiles. The resulting clock correctly determines the age of mouse cohorts, detects the longevity effects of calorie restriction and gene knockouts, and reports rejuvenation of fibroblast-derived iPSCs. The data show that mammalian DNA methylomes are characterized by ...
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