Bayesian Ising Graphical Model for Variable Selection

Fang, Zaili; Kim, In-Young

doi:10.1080/10618600.2015.1035438

Cited by 4 publications

(5 citation statements)

References 23 publications

(10 reference statements)

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“…The key is to extract sufficient quantity of variables, which can be considered as high-dimensional variables combinations. 26,41…”

Section: Resultsmentioning

confidence: 99%

“…The kernel-based machine learning algorithm can be applied on a group of cells with limited sample size. The key is to extract sufficient quantity of variables, which can be considered as high-dimensional variables combinations. , …”

Section: Resultsmentioning

confidence: 99%

“…The key is to extract sufficient quantity of variables, which can be considered as high-dimensional variables combinations. 26,41 Consider n observations for each cell type t, t = 1, ..., T, and p variables data set (y,X t ), where X t = [x 1t ,x 2t , ..., x pt ], x jt = [x j1t ,x j2t , ...,x jnt ] T is an n × 1 vector for the jth variable, j = 1, ...,p. Ridge and Lasso conduct variable selections based on the following generalized linear model for multinomial response,…”

Section: ■ Experimental Sectionmentioning

confidence: 99%

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Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification

et al. 2018

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A high-throughput multiconstriction microfluidic channels device can distinguish human breast cancer cell lines (MDA-MB-231, HCC-1806, MCF-7) from immortalized breast cells (MCF-10A) with a confidence level of ∼81-85% at a rate of 50-70 cells/min based on velocity increment differences through multiconstriction channels aligned in series. The results are likely related to the deformability differences between nonmalignant and malignant breast cells. The data were analyzed by the methods/algorithms of Ridge, nonnegative garrote on kernel machine (NGK), and Lasso using high-dimensional variables, including the cell sizes, velocities, and velocity increments. In kernel learning based methods, the prediction values of 10-fold cross-validations are used to represent the difference between two groups of data, where a value of 100% indicates the two groups are completely distinct and identifiable. The prediction value is used to represent the difference between two groups using the established algorithm classifier from high-dimensional variables. These methods were applied to heterogeneous cell populations prepared using primary tumor and adjacent normal tissue obtained from two patients. Primary breast cancer cells were distinguished from patient-matched adjacent normal cells with a prediction ratio of 70.07%-75.96% by the NGK method. Thus, this high-throughput multiconstriction microfluidic device together with the kernel learning method can be used to perturb and analyze the biomechanical status of cells obtained from small primary tumor biopsy samples. The resultant biomechanical velocity signatures identify malignancy and provide a new marker for evaluation in risk assessment.

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“…The key is to extract sufficient quantity of variables, which can be considered as high-dimensional variables combinations. 26,41…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: ■ Experimental Sectionmentioning

confidence: 99%

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Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification

et al. 2018

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“…. We then update the following ELBO, ELBO = E q (log p(y, 𝜽)) − E q (log q(𝜽)) (12) and iterate the above procedures until ELBO converges.…”

Section: Variational Bayes Inference For Gaussian Process Selectionmentioning

confidence: 99%

Gaussian process selections in semiparametric multi‐kernel machine regression for multi‐pathway analysis

Lin,

Kim

2024

Statistical Analysis

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Analyzing correlated high‐dimensional data is a challenging problem in genomics, proteomics, and other related areas. For example, it is important to identify significant genetic pathway effects associated with biomarkers in which a gene pathway is a set of genes that functionally works together to regulate a certain biological process. A pathway‐based analysis can detect a subtle change in expression level that cannot be found using a gene‐based analysis. Here, we refer to pathway as a set and gene as an element in a set. However, it is challenging to select automatically which pathways are highly associated to the outcome when there are multiple pathways. In this paper, we propose a semiparametric multikernel regression model to study the effects of fixed covariates (e.g., clinical variables) and sets of elements (e.g., pathways of genes) to address a problem of detecting signal sets associated to biomarkers. We model the unknown high‐dimension functions of multi‐sets via multiple Gaussian kernel machines to consider the possibility that elements within the same set interact with each other. Hence, our variable set selection can be considered a Gaussian process set selection. We develop our Gaussian process set selection under the Bayesian variance component‐selection framework. We incorporate prior knowledge for structural sets by imposing an Ising prior on the model. Our approach can be easily applied in high‐dimensional spaces where the sample size is smaller than the number of variables. An efficient variational Bayes algorithm is developed. We demonstrate the advantages of our approach through simulation studies and through a type II diabetes genetic‐pathway analysis.

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“…Another distribution widely used in directional statistics is the Bingham distribution, introduced by Bingham in 1974 [22], is a versatile probability distribution applicable to data distributed across any-dimensional sphere [24]. It finds widespread use across diverse fields such as directional statistics [23] , computer graphics [25] , and neuroscience [26], to model the uncertainty in a parametric form [27] .Parameters of the Bingham distribution include the mean direction, specifying the distribution's central direction, and concentration parameters, regulating the distribution's dispersion around the mean direction and orthogonal directions. Its flexibility allows modeling of both unimodal and multimodal distributions, offering rotational symmetry around the mean direction.…”

Section: Literature Reviewmentioning

confidence: 99%

In-depth Analysis of von Mises Distribution Models: Understanding Theory, Applications, and Future Directions

Benlakhdar,

Rziza,

Oulad Haj Thami

2024

Stat., optim. inf. comput.

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Multimodal and asymmetric circular data manifest in diverse disciplines, underscoring the significance of fitting suitable distributions for the analysis of such data. This study undertakes a comprehensive comparative assessment, encompassing diverse extensions of the von Mises distribution and the associated statistical methodologies, spanning from Richard von Mises' seminal work in 1918 to contemporary applications, with a particular focus on the field of wind energy. The primary objective is to discern the strengths and limitations inherent in each method. To illustrate the practical implications, three authentic datasets and a simulation study are incorporated to showcase the performance of the proposed models. Furthermore, this paper provides an exhaustive list of references pertinent to von Mises distribution models.

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Bayesian Ising Graphical Model for Variable Selection

Cited by 4 publications

References 23 publications

Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification

Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification

Gaussian process selections in semiparametric multi‐kernel machine regression for multi‐pathway analysis

In-depth Analysis of von Mises Distribution Models: Understanding Theory, Applications, and Future Directions

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