A Nested Genetic Algorithm for feature selection in high-dimensional cancer Microarray datasets

Sayed, Sabah; Nassef, Mohammad; Badr, Amr; Farag, Ibrahim

doi:10.1016/j.eswa.2018.12.022

Cited by 191 publications

(109 citation statements)

References 38 publications

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“…The accuracies are 92.31% for the proposed approach using 3 genes, 84.54% for the approach in [24] using 16 CpG-sites, and 87.60% for the NestedGA approach proposed in [28] using 3 genes. Moreover, the proposed approach is simple and fast compared to MSFS [27] and NestedGA [28] as it needs no complicated preprocessing steps. As a step towards the validation of the resultant biomarkers, Fig.…”

Section: Resultsmentioning

confidence: 97%

“…To justify the effectiveness of the proposed approach, it has been compared to the two latest approaches proposed in [24] and [28] applied on the same datasets for Lung cancer Gene Expression and DNAm data. The accuracies are 92.31% for the proposed approach using 3 genes, 84.54% for the approach in [24] using 16 CpG-sites, and 87.60% for the NestedGA approach proposed in [28] using 3 genes. Moreover, the proposed approach is simple and fast compared to MSFS [27] and NestedGA [28] as it needs no complicated preprocessing steps.…”

Section: Resultsmentioning

confidence: 99%

“…A Nested Genetic Algorithm (NestedGA) was proposed in [28] to define the biomarker gene subset by utilizing two types of data; Microarray Gene Expression data and DNAm data. NestedGA consisted of two Genetic Algorithms (GAs); outerGA with SVM fitness function and innerGA with Neural Network fitness.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Building an Ensemble Feature Selection Approach for Cancer Microarray Datasets Using Different Classifiers

Sayed¹,

Nassef²,

Badr³

et al. 2019

IJIES

Self Cite

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The challenge of processing the Microarray datasets with its high dimensionality opened multiple research directions. Different feature selection techniques have been employed to reduce the dimensionality of such Microarray datasets before being attempted by classification algorithms. This study presents an ensemble feature selection approach based on t-test and Genetic Algorithm with five different classification algorithms as its fitness function: Support Vector Machine, Random Forest, Nearest Centroid, K Nearest Neighbour, and Maximum Likelihood with 5fold cross validation. The proposed approach has been applied on two different datasets for Lung cancer; Microarray Gene Expression and DNA methylation datasets aiming to find the Lung cancer biomarker genes. The experimental results showed that the three genes (DLX5, KRT5, and SELENBP1) resulted from processing both datasets have higher classification accuracy (92.31%) compared to separately processing the Gene Expression and the DNA methylation datasets with accuracies 90.38% and 86.54% respectively. Moreover, the classification accuracy achieved using the three aforementioned genes could not be achieved by other research studies unless by using more genes.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Building an Ensemble Feature Selection Approach for Cancer Microarray Datasets Using Different Classifiers

Sayed¹,

Nassef²,

Badr³

et al. 2019

IJIES

Self Cite

View full text Add to dashboard Cite

show abstract

“…In enhanced reliefF algorithm, the quality estimation   Q a is updated using the Eq. (1), (2), and…”

Section: B Gene Selection Using Enhanced Relieff Algorithmmentioning

confidence: 99%

“…In present decades, lung cancer is the common cause of death among people. So, the early recognition of lung cancer increases the chance of survival rate [1][2]. Presently, genelevel treatment is a promising technique, which effectively identifies the normal and abnormal patients [3].…”

Section: Introductionmentioning

confidence: 99%

Optimal Classification of Lung Cancer Related Genes using Enhanced reliefF Algorithm and Multiclass Support Vector Machine

Patil¹,

Patil²,

Prabhakar³

2019

IJITEE

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Currently, the automatic lung cancer classification remains a challenging issue for the researchers, due to noisy gene expression data, high dimensional data, and the small sample size. To address these problems, an enhanced gene selection algorithm and multiclass classifier are developed. In this research, the lung cancer-related genes (GEO IDs: GSE10245, GSE19804, GSE7670, GSE10072, and GSE6044) were collected from Gene Expression Omnibus (GEO) dataset. After acquiring the lung cancer-related genes, gene selection was carried out by using enhanced reliefF algorithm for selecting the optimal genes. In enhanced reliefF gene selection algorithm, earthmover distance measure and firefly optimizer were used instead of Manhattan distance measure for identifying the nearest miss and nearest hit instances, which significantly lessens the “curse of dimensionality” issue. These optimal genes were given as the input for Multiclass Support Vector Machine (MSVM) classifier for classifying the sub-classes of lung cancer. The experimental section showed that the proposed system improved the classification accuracy up to 3-10% related to the existing systems in light of accuracy, False Positive Rate (FPR), error rate, and True Positive Rate (TPR).

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Feature Selection and Machine Learning Models for High‐Dimensional Data: State‐of‐the‐Art

Manikandan

Abirami

2021

Computational Intelligence and Healthcare Informatics

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A Nested Genetic Algorithm for feature selection in high-dimensional cancer Microarray datasets

Cited by 191 publications

References 38 publications

Building an Ensemble Feature Selection Approach for Cancer Microarray Datasets Using Different Classifiers

Building an Ensemble Feature Selection Approach for Cancer Microarray Datasets Using Different Classifiers

Optimal Classification of Lung Cancer Related Genes using Enhanced reliefF Algorithm and Multiclass Support Vector Machine

Feature Selection and Machine Learning Models for High‐Dimensional Data: State‐of‐the‐Art

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