Gene selection for microarray data classification via multi-objective graph theoretic-based method

Rostami, Mehrdad; Forouzandeh, Saman; Berahmand, Kamal; Soltani, Mina; Shahsavari, Meisam; Oussalah, Mourad

doi:10.1016/j.artmed.2021.102228

Cited by 89 publications

(24 citation statements)

References 77 publications

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“…Since this dataset contains features with missing values, to handle these missing data in our experiments, we replaced each missing datum with the mean of the available data on the feature set [ 94 ].…”

Section: Resultsmentioning

confidence: 99%

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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Section: Resultsmentioning

confidence: 99%

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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“…SVM, from (B) 2 s to (C) 1 h and (D) 13 h, and iii. even for kNN, for which the runtime increases by (C) 300 and (D) almost 800 times with respect to (B), despite its simplicity and the limited range [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ] in which k was optimized. Again, it is worth remarking that each time corresponds to a single run and that we advise running several resamplings/bootstraps (as in this study) to attain stable outcomes.…”

Section: Resultsmentioning

confidence: 99%

“…While traditional diagnoses and prognoses are built on a combination of clinical and physical examination and medical history, research is increasingly relying on in silico procedures based on high-throughput gene expression data to detect disease [ 1 , 2 ]. Feature selection is a crucial process in the fast growing fields of pattern recognition and machine learning [ 3 ], while classification is the supervised learning task of predicting the categories of new observations on the basis of training data.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study

Zanella

Facco

Bezzo

et al. 2022

IJMS

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The classification of high dimensional gene expression data is key to the development of effective diagnostic and prognostic tools. Feature selection involves finding the best subset with the highest power in predicting class labels. Here, we conducted a comparative study focused on different combinations of feature selectors (Chi-Squared, mRMR, Relief-F, and Genetic Algorithms) and classification learning algorithms (Random Forests, PLS-DA, SVM, Regularized Logistic/Multinomial Regression, and kNN) to identify those with the best predictive capacity. The performance of each combination is evaluated through an empirical study on three benchmark cancer-related microarray datasets. Our results first suggest that the quality of the data relevant to the target classes is key for the successful classification of cancer phenotypes. We also proved that, for a given classification learning algorithm and dataset, all filters have a similar performance. Interestingly, filters achieve comparable or even better results with respect to the GA-based wrappers, while also being easier and faster to implement. Taken together, our findings suggest that simple, well-established feature selectors in combination with optimized classifiers guarantee good performances, with no need for complicated and computationally demanding methodologies.

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“…In SVM, mRMR was utilized as a fitness function (FF) under the presented technique for selecting relevant features that are used for estimating the prediction accuracy and classifying cancer correctly. In reference [ 16 ], a novel social network analysis-based GS method was presented. The presented approach contains 2 important objectives: relevance maximization and redundancy minimization of chosen genes.…”

Section: Related Workmentioning

confidence: 99%

Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification

Hilal

Malibari

Obayya

et al. 2022

Computational Intelligence and Neuroscience

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Recently, bioinformatics and computational biology-enabled applications such as gene expression analysis, cellular restoration, medical image processing, protein structure examination, and medical data classification utilize fuzzy systems in offering effective solutions and decisions. The latest developments of fuzzy systems with artificial intelligence techniques enable to design the effective microarray gene expression classification models. In this aspect, this study introduces a novel feature subset selection with optimal adaptive neuro-fuzzy inference system (FSS-OANFIS) for gene expression classification. The major aim of the FSS-OANFIS model is to detect and classify the gene expression data. To accomplish this, the FSS-OANFIS model designs an improved grey wolf optimizer-based feature selection (IGWO-FS) model to derive an optimal subset of features. Besides, the OANFIS model is employed for gene classification and the parameter tuning of the ANFIS model is adjusted by the use of coyote optimization algorithm (COA). The application of IGWO-FS and COA techniques helps in accomplishing enhanced microarray gene expression classification outcomes. The experimental validation of the FSS-OANFIS model has been performed using Leukemia, Prostate, DLBCL Stanford, and Colon Cancer datasets. The proposed FSS-OANFIS model has resulted in a maximum classification accuracy of 89.47%.

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Gene selection for microarray data classification via multi-objective graph theoretic-based method

Cited by 89 publications

References 77 publications

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study

Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification

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