A novel hybrid gene selection for tumor identification by combining multifilter integration and a recursive flower pollination search algorithm

Li, Min; Ke, Lin; Wang, Lei; Deng, Shaobo; Yu, Xiang

doi:10.1016/j.knosys.2022.110250

Cited by 21 publications

(4 citation statements)

References 47 publications

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“…The final steps ensure the convergence of the subgradient algorithm. In the following, we compare different forms of the step (α k ), specifically those of the form 1 k γ with γ ∈ [0.05, 1]. Figure 3 illustrates how the objective function changes with the number of iterations for different values of γ.…”

Section: Choice Of (α K )mentioning

confidence: 99%

“…Currently, advancements in technology have made massive datasets, like DNA microarray data, fundamental to statistical analysis [1,2]. High-dimensional data pose challenges in classification tasks and gene subset identification [3].…”

Section: Introductionmentioning

confidence: 99%

“…In cancer classification, logistic regression is used to model the relationship between a set of independent variables (genes) and a binary dependent variable (tumor or normal class) by minimizing the function g(β) in Equation (1).…”

mentioning

confidence: 99%

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GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

Bir-Jmel,

Douiri,

Bernoussi

et al. 2024

Computers

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Advancements in genomic technologies have paved the way for significant breakthroughs in cancer diagnostics, with DNA microarray technology standing at the forefront of identifying genetic expressions associated with various cancer types. Despite its potential, the vast dimensionality of microarray data presents a formidable challenge, necessitating efficient dimension reduction and gene selection methods to accurately identify cancerous tumors. In response to this challenge, this study introduces an innovative strategy for microarray data dimension reduction and crucial gene set selection, aiming to enhance the accuracy of cancerous tumor identification. Leveraging DNA microarray technology, our method focuses on pinpointing significant genes implicated in tumor development, aiding the development of sophisticated computerized diagnostic tools. Our technique synergizes gene selection with classifier training within a logistic regression framework, utilizing a generalized Fused LASSO (GFLASSO-LR) regularizer. This regularization incorporates two penalties: one for selecting pertinent genes and another for emphasizing adjacent genes of importance to the target class, thus achieving an optimal trade-off between gene relevance and redundancy. The optimization challenge posed by our approach is tackled using a sub-gradient algorithm, designed to meet specific convergence prerequisites. We establish that our algorithm’s objective function is convex, Lipschitz continuous, and possesses a global minimum, ensuring reliability in the gene selection process. A numerical evaluation of the method’s parameters further substantiates its effectiveness. Experimental outcomes affirm the GFLASSO-LR methodology’s high efficiency in processing high-dimensional microarray data for cancer classification. It effectively identifies compact gene subsets, significantly enhancing classification performance and demonstrating its potential as a powerful tool in cancer research and diagnostics.

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Section: Choice Of (α K )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

Bir-Jmel,

Douiri,

Bernoussi

et al. 2024

Computers

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show abstract

“…This approach has two stages: the first stage involves using one-class SVM for detec2ng anomalies, and the second stage involves developing a guided GA to iden2fy the best subset of genes. Moreover, the authors in [33] developed a two-phase gene selec2on method. Firstly, an ensemble method that combined numerous filter-based methods, including the chi-square test, informa2on gain ra2o, and ReliefF, was developed.…”

Section: Introductionmentioning

confidence: 99%

A hybrid of Information gain and a Coati Optimization Algorithm for gene selection in microarray gene expression data classification.

Osama

Ali

Shaban

2023

Kafrelsheikh Journal of Information Sciences

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Gene expression data has become an essen2al tool for cancer classifica2on because it provides substan2al insights into the underlying mechanisms of cancer progression. However, the high-dimensional nature of microarray gene expression data presents a significant challenge. This paper introduces a new method called IG-COA, which combines Informa2on Gain (IG) approach and Coa2 Op2miza2on Algorithm (COA), to iden2fy the biomarkers genes. COA is a recent algorithm that has not been previously examined for feature or gene selec2on, to the best of our knowledge. Firstly, the IG method is used because using COA directly on microarray datasets is ineffec2ve and can make it challenging to train a classifier accurately. Secondly, the COA algorithm is u2lized to select the op2mal subset of genes from the previously selected ones. The effec2veness of the suggested IG-COA method with a Support Vector Machine is tested on several microarray gene expression datasets, and it exceeds other state-of-the-art methods.

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Optimizing Microarray Gene Selection in Colon Cancer: An Enhanced Metaheuristic Algorithm for Feature Selection

Benghazouani,

Nouh,

Zakrani

2024

Communications in Computer and Information Science

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A novel hybrid gene selection for tumor identification by combining multifilter integration and a recursive flower pollination search algorithm

Cited by 21 publications

References 47 publications

GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification

A hybrid of Information gain and a Coati Optimization Algorithm for gene selection in microarray gene expression data classification.

Optimizing Microarray Gene Selection in Colon Cancer: An Enhanced Metaheuristic Algorithm for Feature Selection

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