NDLI: Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent

Content Provider	Springer Nature : BioMed Central
Author	Klosa, Jan Simon, Noah Westermark, Pål Olof Liebscher, Volkmar Wittenburg, Dörte
Abstract	Background Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalization 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 observations or for some biological reason. Then, the model goodness of fit is penalized 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 realized by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally, seagull -the R package presented here- produces complete regularization paths. Results Publicly available high-dimensional methylation data are used to compare seagull to the established R package SGL. The results of both packages enabled a precise prediction of biological age from DNA methylation status. But even though the results of seagull and SGL were very similar (R2 > 0.99), seagull computed the solution in a fraction of the time needed by SGL. Additionally, seagull enables the incorporation of weights for each penalized feature. Conclusions The following operators for linear regression models are available in seagull: lasso, group lasso, sparse-group lasso and Integrative LASSO with Penalty Factors (IPF-lasso). Thus, seagull is a convenient envelope of lasso variants.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-020-03725-w.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-020-03725-w
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2020-09-15
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Optimization Machine learning High-dimensional data R package Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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

AUTALASSO: an automatic adaptive LASSO for genome-wide prediction

eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models

CNVDeep: deep association of copy number variants with neurocognitive disorders

Time-lagged Ordered Lasso for network inference

Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies

Sparse sliced inverse regression for high dimensional data analysis

pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms

A flexible framework for sparse simultaneous component based data integration

Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent

Similar Documents

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

AUTALASSO: an automatic adaptive LASSO for genome-wide prediction

eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models

CNVDeep: deep association of copy number variants with neurocognitive disorders

Time-lagged Ordered Lasso for network inference

Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies

Sparse sliced inverse regression for high dimensional data analysis

pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms

A flexible framework for sparse simultaneous component based data integration

Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent