A Survey of Gene Selection and Classification Techniques Based on Cancer Microarray Data Analysis

Zhang, Xinman; He, Tingting; Ouyang, Liwei; Xu, Xuebin; Chen, Sushing

doi:10.1109/compcomm.2018.8781078

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In the first stage the dimensionality is reduced using a feature selection technique embedded within a classification model and in the second stage, a standard classification technique is applied to the resulting set of features. The selection step is followed by a predictive model learning step [14,15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Feature Selection Method for Improving Classification of Microarray Gene Expression Data

T.¹,

M.²,

N³

2023

IJSRCSEIT

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Microarray gene expression profiles provide valuable answers to a variety of problems, and contributes to advances in clinical medicine. Gene expression data typically has a high dimension and a small sample size. Gene selection from microarray gene expression data is a challenge due to high dimensionality of the data. The number of samples in the microarray dataset is much smaller compared to the number of genes as features. To extract useful gene information from cancer microarray data and reduce dimensionality, selection of significant genes is necessary. An effective method of gene feature selection helps in dimensionality reduction and improves the classification performance. Experimental results suggest that appropriate combination of filter gene selection methods is more effective than individual techniques for microarray data classification. In this paper, we propose a two-layered feature selection method. In the first layer, t-test statistical method is used to remove the features that have little correlation with the classification results. In the second layer, line segment approximation method is used to transform the feature subset into a less dimensional feature space. Four well known classifiers kNN, SVM, NBC, DT were used to verify the performance of the proposed feature selection algorithm on binary class microarray data. The experimental results show that the proposed method can effectively select relevant gene subsets, and achieves higher classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Multilevel Feature Selection Method for Improving Classification of Microarray Gene Expression Data

T.¹,

M.²,

N³

2023

IJSRCSEIT

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Experimental Study on Performance of Symbolic Classifier With Gene Selection Methods for Multiclass Microarray Gene Expression Data

T¹

2022

ijarcs

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Microarray is a useful technique for measuring expression data of thousands of genes simultaneously. The expression level of genes is known to contain the keys to address fundamental problems relating to the prevention and cure of diseases, biological evolution mechanisms and drug discovery. Previous research has demonstrated that this technology can be useful in the classification of cancers. Most proposed cancer classification methods work well only on binary class problems and not extensible to multi-class problems. This work is an attempt to classify high dimensional, multiclass Microarray Gene expression data using symbolic classifier.

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Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data

Qiu,

Heidari,

Chen

et al. 2024

Sci Rep

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Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

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A Survey of Gene Selection and Classification Techniques Based on Cancer Microarray Data Analysis

Cited by 4 publications

References 14 publications

Multilevel Feature Selection Method for Improving Classification of Microarray Gene Expression Data

Multilevel Feature Selection Method for Improving Classification of Microarray Gene Expression Data

Experimental Study on Performance of Symbolic Classifier With Gene Selection Methods for Multiclass Microarray Gene Expression Data

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data

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