Sparse Laplacian Shrinkage with the Graphical Lasso Estimator for Regression Problems

Xia, Siwei; Yang, Yuehan; Yang, Hu

doi:10.1007/s11749-021-00779-7

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…In practical data analysis, acquiring the precise network structure as represented by the Laplacian matrix poses significant challenges and often proves unattainable. Prior studies primarily focused on employing Laplacian matrices derived solely from the data itself 22 . In this study, the network directly computed based on the correlation structure of the input gene expression data is referred to as reference-free network.…”

Section: Bi-network Regularization Modelmentioning

confidence: 99%

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning

Yao,

Li,

et al. 2024

Preprint

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The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven for precision genomics.

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Section: Bi-network Regularization Modelmentioning

confidence: 99%

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning

Yao,

Li,

et al. 2024

Preprint

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show abstract

“…Further, there are many studies about the Laplacian penalty. For example, Xia et al 21 proposed a two-step method called Sparse Laplacian Shrinkage with the Graphical Lasso Estimator. Compared with that, the proposed method applies the Laplacian penalty to the iterative algorithm.…”

Section: Model and Algorithmmentioning

confidence: 99%

Local linear approximation with Laplacian smoothing penalty and application in biology

Chen

Yang

2023

Stat Methods Med Res

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Highly correlated structures appear in various fields, such as biology, biochemistry, and finance, with challenges of dimensionality and sparse estimation. To solve this problem, we propose an algorithm called local linear approximation with the Laplacian smoothing penalty (LLA-LSP). This method produces an accurate and smooth estimate that incorporates the correlation structure among predictors. We compare and discuss the difference between the Laplacian smoothing penalty and the total variance penalty. We prove that this algorithm converges to the oracle solution in a few iterations with a large probability. Numerical results show that the LLA-LSP has good performance in both variable selection and estimation. We apply the proposed algorithm to two biological datasets, a gene expression dataset and a chemical protein dataset, and provide meaningful insights.

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Transfer learning on stratified data: joint estimation transferred from strata

Gao

Yang

2023

Pattern Recognition

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Sparse Laplacian Shrinkage with the Graphical Lasso Estimator for Regression Problems

Cited by 4 publications

References 28 publications

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning

Local linear approximation with Laplacian smoothing penalty and application in biology

Transfer learning on stratified data: joint estimation transferred from strata

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