Results 31 to 40 of about 2,762 (131)

Microbial metabolites in tumor epigenetic regulation

iMeta, Volume 5, Issue 1, February 2026.
The gut microbiome modulates tumor epigenetic regulation through bioactive metabolites derived from dietary substrates. Microbiota‐produced SCFAs, secondary BAs, one‐carbon metabolites, and tryptophan‐derived ligands regulate histone acetylation, DNA methylation, and chromatin remodeling via HDAC, DNMT, AhR, and metabolic cofactor‐dependent pathways ...
Wangzheqi Zhang, Haoling Zhang, Yan Liao, Shuya Jiang, Haolong Zhang, Chenwei Huang, Zhijing Song, Rui Zhao, Ping Lu, Xiaohong Kang, Eryan Kong, Yalin Zhu, Wei Wang, Xuesong Liu, Yisheng Chen, Zhiwen Luo, Zhaoyu Li, Di Wang, Na Xing, Yadong Guo, Jingjing Zhang, Xiaojing Wang, Wenyi Wang, Mowaffaq Adam, Bakiah Shaharuddin, Muhamad Yusri Musa, Doblin Sandai, Chenglong Zhu, Aimin Jiang, Peng Luo, Zhijie Zhao, Zui Zou   +31 more
wiley   +1 more source

Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

, 2013
Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as
A. Chiang, A. Puig, B. Krishnapuram, C. Zhang, D. Bertsekas, D. Donoho, D. Hunter, H. Gao, H. Zou, J. Fan, J. Friedman, J. Huang, J.H. Friedman, Jian Huang, K. Lange, L. Meier, L. Wang, L. Wang, M. Yuan, N. Simon, P. Breheny, P. Ravikumar, P. Tseng, Patrick Breheny, R. Tibshirani, T. Scheetz, T. Wu, W. Fu, Y. She   +28 more
core   +1 more source

Benchmarking Sparse Variable Selection Methods for Genomic Data Analyses

Statistics in Medicine, Volume 45, Issue 3-5, February 2026.
ABSTRACT Genomics and other studies encounter many features and a selection of essential features with high accuracy is desired. In recent years, there has been a significant advancement in the use of Bayesian inference for variable (or feature) selection.
Hema Sri Sai Kollipara, Tapabrata Maiti, Sanjukta Chakraborty, Samiran Sinha   +3 more
wiley   +1 more source

Discussion: One-step sparse estimates in nonconcave penalized likelihood models

, 2007
Discussion of ``One-step sparse estimates in nonconcave penalized likelihood models'' [arXiv:0808.1012]Comment: Published in at http://dx.doi.org/10.1214/07-AOS0316C the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of ...
Zhang, Cun-Hui
core   +2 more sources

Multi-Attribute Graph Estimation With Sparse-Group Non-Convex Penalties

IEEE Access
We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar ...
Jitendra K. Tugnait
doaj   +1 more source

Optimistic Robust Optimization With Applications To Machine Learning [PDF]

, 2017
Robust Optimization has traditionally taken a pessimistic, or worst-case viewpoint of uncertainty which is motivated by a desire to find sets of optimal policies that maintain feasibility under a variety of operating conditions. In this paper, we explore
Mafusalov, Alexander, Norton, Matthew, Takeda, Akiko   +2 more
core   +1 more source

Lasso Penalization for High‐Dimensional Beta Regression Models: Computation, Analysis, and Inference

Stat, Volume 14, Issue 4, December 2025.
ABSTRACT Beta regression is commonly employed when the outcome variable is a proportion. Since its conception, the approach has been widely used in applications spanning various scientific fields. A series of extensions have been proposed over time, several of which address variable selection and penalized estimation, e.g., with an ℓ1$$ {\ell}_1 ...
Niloofar Ramezani, Martin Slawski
wiley   +1 more source

Robust Distance Correlation for Variable Screening

Stat, Volume 14, Issue 3, September 2025.
ABSTRACT In modern statistical applications, identifying critical features in high‐dimensional data is essential for scientific discoveries. Traditional best subset selection methods face computational challenges, while regularization approaches such as Lasso, SCAD and their variants often exhibit poor performance with ultrahigh‐dimensional data.
Tianzhou Ma, Fan Yang, Hongjie Ke, Zhao Ren   +3 more
wiley   +1 more source

Sparse Solution of Underdetermined Linear Equations via Adaptively Iterative Thresholding [PDF]

, 2013
Finding the sparset solution of an underdetermined system of linear equations $y=Ax$ has attracted considerable attention in recent years. Among a large number of algorithms, iterative thresholding algorithms are recognized as one of the most efficient ...
Lin, Shaobo, Xu, Zongben, Zeng, Jinshan
core  

α$$ \alpha $$‐KIDS: A Novel Feature Evaluation in the Ultrahigh‐Dimensional Right‐Censored Setting, With Application to Head and Neck Cancer

Statistics in Medicine, Volume 44, Issue 15-17, July 2025.
ABSTRACT Recent advances in sequencing technologies have allowed the collection of massive genome‐wide information that substantially enhances the diagnosis and prognosis of head and neck cancer. Identifying predictive markers for survival time is crucial for devising prognostic systems and learning the underlying molecular drivers of the cancer course.
Atika Farzana Urmi, Chenlu Ke, Dipankar Bandyopadhyay   +2 more
wiley   +1 more source