Selecting Biomarkers for Building Optimal Treatment Selection Rules by Using Kernel Machines

Dasgupta, Sayan; Huang, Ying

doi:10.1111/rssc.12379

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…The direct methods use a weighted classification framework and are through the support vector machine (SVM) approaches. The direct methods include Outcome Weighted Learning [Zhao et al 2012], Residual Weighted Learning [Zhou et al 2017, and other variational forms [Dasgupta andHuang 2020, Mo et al 2021]. These methods, however, are often confined by the limitation of the SVM procedure, e.g., the difficulty with a small separation margin, choices of kernels, etc.…”

Section: Introductionmentioning

confidence: 99%

Data-guided Treatment Recommendation with Feature Scores

Chen¹,

Wang²,

Song³

et al. 2022

STAT SINICA

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Despite the availability of large amounts of genomics data, medical treatment recommendations have not successfully used them. In this paper, we consider the utility of high dimensional genomic-clinical data and nonparametric methods for making cancer treatment recommendations. This builds upon the framework of the individualized treatment rule [Qian and Murphy 2011] but we aim to overcome their method's limitations, specifically in the instances when the method encounters a large number of covariates and an issue of model misspecification. We tackle this problem using a dimension reduction method, namely Sliced Inverse Regression (SIR, [Li 1991]), with a rich class of models for the treatment response. Notably, SIR defines a feature space for high-dimensional data, offering an advantage similar to those found in the popular neural network models. With the features obtained from SIR, a simple visualization is used to compare different treatment options and present the recommended treatment.Additionally, we derive the consistency and the convergence rate of the proposed rec-1

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Section: Introductionmentioning

confidence: 99%

Data-guided Treatment Recommendation with Feature Scores

Chen¹,

Wang²,

Song³

et al. 2022

STAT SINICA

View full text Add to dashboard Cite

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Feature selection for support vector regression using a genetic algorithm

et al. 2021

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Summary Support vector regression (SVR) is particularly beneficial when the outcome and predictors are nonlinearly related. However, when many covariates are available, the method’s flexibility can lead to overfitting and an overall loss in predictive accuracy. To overcome this drawback, we develop a feature selection method for SVR based on a genetic algorithm that iteratively searches across potential subsets of covariates to find those that yield the best performance according to a user-defined fitness function. We evaluate the performance of our feature selection method for SVR, comparing it to alternate methods including LASSO and random forest, in a simulation study. We find that our method yields higher predictive accuracy than SVR without feature selection. Our method outperforms LASSO when the relationship between covariates and outcome is nonlinear. Random forest performs equivalently to our method in some scenarios, but more poorly when covariates are correlated. We apply our method to predict donor kidney function 1 year after transplant using data from the United Network for Organ Sharing national registry.

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Selecting Biomarkers for Building Optimal Treatment Selection Rules by Using Kernel Machines

Cited by 2 publications

References 31 publications

Data-guided Treatment Recommendation with Feature Scores

Data-guided Treatment Recommendation with Feature Scores

Feature selection for support vector regression using a genetic algorithm

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