The L regularization network Cox model for analysis of genomic data

Jiang, Hong-Kun; Liang, Yong

doi:10.1016/j.compbiomed.2018.07.009

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…( 1), such that the size of variable coefficients can be controlled. Several penalty terms have been discussed in the literature considering the Cox proportional hazards model [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. The LASSO method, proposed by Tibshirani [6], is one of the popular penalty terms.…”

Section: Panelized Cox Proportional Hazards Modelmentioning

confidence: 99%

An Investigational Modeling Approach for Improving Gene Selection using Regularized Cox Regression Model

Abdallh¹,

Algamal²

2023

Math.Biol.Bioinf.

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By producing the required proteins, the process of gene expression establishes the physical properties of living things. Gene expression from DNA or RNA may be recorded using a variety of approaches. Regression analysis has evolved in prominence in the area of genetic research recently. Several of the genes in high dimensional gene expression information for statistical inference may not be related to their illnesses, which is one of the major problems. The ability of gene selection to enhance the outcomes of several techniques has been demonstrated. For censored survival data, the Cox proportional hazards regression model is the most widely used model. In order to identify important genes and achieve high classification accuracy, a new technique for selecting the tuning parameter is suggested in this study using an optimization algorithm. According to experimental findings, the suggested strategy performs much better than the two rival methods in terms of the area under the curve and the number of chosen genes. This study provides a comprehensive assessment of the latest work on performance evaluation of regression analysis in gene selection. In addition to its performance analysis, this research conducts a thorough assessment of the numerous efforts done on various extended models based on gene selection in recent years.

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Section: Panelized Cox Proportional Hazards Modelmentioning

confidence: 99%

An Investigational Modeling Approach for Improving Gene Selection using Regularized Cox Regression Model

Abdallh¹,

Algamal²

2023

Math.Biol.Bioinf.

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“…Nowadays, the data of gene expression are increasingly applied to different clinical outcomes in order to facilitate disease diagnosis. Such these data are high dimensional where the number of genes exceeds the number of observations [74]. Regression technique is a standard practice to study jointly the influence of multiple predictors on a response.…”

Section: Experimental Series 2: Real Application Of Aoagamentioning

confidence: 99%

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

et al. 2021

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Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.

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“…Therefore, they improve the prediction capabilities and better address the inherent structure of omics data. The papers [19,20] introduced the idea using linear models, followed by DrCOX [21,22], AdaLNet [23], Net-Cox [24], L 1/2 penalty [25], and DegreeCox [26] generalized to Cox regression. In [10], the authors described the advantages and limits of penalized methods in the context of linear models, logistic regression, and Cox regression, and [6] provides a large benchmark study with eleven methods (including penalization approaches) for survival analysis.…”

Section: Introductionmentioning

confidence: 99%

COSMONET: An R Package for Survival Analysis Using Screening-Network Methods

et al. 2021

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Identifying relevant genomic features that can act as prognostic markers for building predictive survival models is one of the central themes in medical research, affecting the future of personalized medicine and omics technologies. However, the high dimension of genome-wide omic data, the strong correlation among the features, and the low sample size significantly increase the complexity of cancer survival analysis, demanding the development of specific statistical methods and software. Here, we present a novel R package, COSMONET (COx Survival Methods based On NETworks), that provides a complete workflow from the pre-processing of omics data to the selection of gene signatures and prediction of survival outcomes. In particular, COSMONET implements (i) three different screening approaches to reduce the initial dimension of the data from a high-dimensional space p to a moderate scale d, (ii) a network-penalized Cox regression algorithm to identify the gene signature, (iii) several approaches to determine an optimal cut-off on the prognostic index (PI) to separate high- and low-risk patients, and (iv) a prediction step for patients’ risk class based on the evaluation of PIs. Moreover, COSMONET provides functions for data pre-processing, visualization, survival prediction, and gene enrichment analysis. We illustrate COSMONET through a step-by-step R vignette using two cancer datasets.

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The L regularization network Cox model for analysis of genomic data

Cited by 6 publications

References 27 publications

An Investigational Modeling Approach for Improving Gene Selection using Regularized Cox Regression Model

An Investigational Modeling Approach for Improving Gene Selection using Regularized Cox Regression Model

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

COSMONET: An R Package for Survival Analysis Using Screening-Network Methods

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