ArticleBMC bioinformatics2020
Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Predicting the magnitude of risk for non-curative endoscopic submucosal dissection in superficial esophageal cancer using explainable artificial intelligence.World journal of gastrointestinal oncology · 2026Article
- MyESL: A Software for Evolutionary Sparse Learning in Molecular Phylogenetics and Genomics.Molecular biology and evolution · 2025Article
- Comparison of random survival forest and Cox regression for long-term prognosis of pediatric Wilms tumor: based on the SEER database and external validation cohort from China.Discover oncology · 2025Article
- Transfer Learning for Error-Contaminated Poisson Regression Models.Statistics in medicine · 2025Article
- Causality-driven candidate identification for reliable DNA methylation biomarker discovery.Nature communications · 2025Article
- Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.Frontiers in immunology · 2025Article
- HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data.PLoS computational biology · 2025Article
- Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections.Cancers · 2024Article
- Deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial sections.bioRxiv : the preprint server for biology · 2024Article
- A Novel Algorithm for Feature Selection Using Penalized Regression with Applications to Single-Cell RNA Sequencing Data.Biology · 2022Article
- LTBP2 inhibits prostate cancer progression and metastasis via the PI3K/AKT signaling pathway.Experimental and therapeutic medicine · 2022Article
- Factors related to early and rapid assessment of in-hospital mortality among older adult trauma patients in an earthquake.World journal of emergency medicine · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundStatistical 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.
resultsPublicly 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 (R
conclusionsThe 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.