Data-Mining Techniques to Classify Microarray Gene Expression Data Using Gene Selection by SVD and Information Gain

Vural, Halit; Subaşı, Abdülhamit

doi:10.13187/mai.2015.6.171

Cited by 23 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike that for lung cancer and DLBCL, the classification accuracy for colon cancer was relatively low, which was 90.32% for IG-SVM. Nonetheless the classification accuracy achieved in this study for colon cancer was still higher than that reported in another study (83.87%), which used the same number of selected genes by singular value decomposition and IG [18] . Since default settings were used for various tools, the possibility to achieve high accuracy with altered settings and selections can’t be ruled out.…”

Section: Resultscontrasting

confidence: 80%

“…We then applied the filter methods, namely IG, gain ratio, reliefF, and correlation, for gene selection. The required number of genes selected cannot be determined using a common standard, but several hundred of genes are demonstrated to be sufficient to achieve high accuracy [18] . Therefore, different numbers of genes are selected for different filters with the number of genes ranging from 1 to 200.…”

Section: Resultsmentioning

confidence: 99%

“…SVM is a machine-learning algorithm based on the principle of structural risk minimization [17] . It shows superior classification performance with better global minimization and generalization ability than traditional classifiers [18] . Therefore, SVM-based methods have been commonly developed for the selection and classification of genes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification

Gao

et al. 2017

Genomics, Proteomics &Amp; Bioinformatics

106

View full text Add to dashboard Cite

It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine (IG-SVM) in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYL9, and GUCA2B.

show abstract

Section: Resultscontrasting

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification

Gao

et al. 2017

Genomics, Proteomics &Amp; Bioinformatics

106

View full text Add to dashboard Cite

show abstract

“…Colon cancer classification produced by ISIG is better than the ones reported in the literature [9,11,14,29,30]. Even though Alshamlan [5] and Motieghader et al [16] obtained slightly higher accuracy for Colon cancer classification, yet they are of larger gene's subset.…”

Section: Resultsmentioning

confidence: 78%

Incremental Search for Informative Gene Selection in Cancer Classification

Fajila¹,

Yusof²

2021

AETiC

View full text Add to dashboard Cite

Although numerous methods of using microarray data analysis for classification have been reported, there is space in the field of cancer classification for new inventions in terms of informative gene selection. This study introduces a new incremental search-based gene selection approach for cancer classification. The strength of wrappers in determining relevant genes in a gene pool can be increased as they evaluate each possible gene’s subset. Nevertheless, the searching algorithms play a major role in gene’s subset selection. Hence, there is the possibility of finding more informative genes with incremental application. Thus, we introduce an approach which utilizes two searching algorithms in gene’s subset selection. The approach was efficient enough to classify five out of six microarray datasets with 100% accuracy using only a few biomarkers while the rest classified with only one misclassification.

show abstract

“…Further, in the classification of colon cancer, a sparse representation based method is proposed in [3] which provide 91.94% accuracy; nevertheless with a very huge gene subset. Moreover, the accuracies for colon cancer dataset and SRBCT are greater than [9,29]. At the same time the performance of SRBCT is greater than that of reported in [30].…”

Section: B Experimental Resultsmentioning

confidence: 86%

The Effect of Evolutionary Algorithm in Gene Subset Selection for Cancer Classification

Fajila¹,

Jahan²

2018

IJMECS

View full text Add to dashboard Cite

The fact that reflects the cancer research consequences shows that still there are improvements that should be investigated in the stream of cancer in future. This leads the researchers to actively involve further in cancer research field. As an invention, a hybrid machine learning method is proposed in this study where two filters are assessed along with a wrapper approach. Typically, filters prioritize the features while, wrappers contribute in subset identification. Though both filters and wrappers exist independently, the excellent results they produce when applied subsequently. The wrapperfilter combination plays a major role in feature selection. Yet, incorporating with a best strategy for feature space analysis is crucial in this concern. Thus, we introduce the Evolutionary Algorithm in the proposed study to search through the feature space for informative gene subset selection. Though there are several gene selection approaches for cancer classification, many of them suffer from law classification accuracy and huge gene subset for prediction. Hence, we propose Evolutionary Algorithm to overcome this problem. The proposed approach is evaluated on five microarray datasets, where three out of them provide 100% accuracy. Regardless the number of genes selected, both filters provide the same performance throughout the datasets used. As a consequence, the Evolutionary Algorithm in feature space search is highlighted for its performance in gene subset selection.

show abstract

Data-Mining Techniques to Classify Microarray Gene Expression Data Using Gene Selection by SVD and Information Gain

Cited by 23 publications

References 11 publications

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification

Incremental Search for Informative Gene Selection in Cancer Classification

The Effect of Evolutionary Algorithm in Gene Subset Selection for Cancer Classification

Contact Info

Product

Resources

About