Hybrid Feature Selection Algorithm mRMR-ICA for Cancer Classification from Microarray Gene Expression Data

Wang, Shuaiqun; Kong, Wei; Aorigele,; Deng, Jin; Gao, Shangce; Zeng, Weiming

doi:10.2174/1386207321666180601074349

Cited by 15 publications

(6 citation statements)

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“…The maximum-relevance minimum redundancy (mRMR) algorithm was used to extract the robust features in the training set. Maximum relevance allowed for the selection of features most associated with pCRs (20), while minimum redundancy allowed for the selection of features with minimal redundancy among the others. Optimal features set with high correlation and low redundancy were obtained using the mRMR algorithm.…”

Section: Establishment Of An Optimal Radiomics Signature Based On Macmentioning

confidence: 99%

Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

Chen

Shu

et al. 2020

Front. Oncol.

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The construction and validation of a radiomics nomogram based on machine learning using magnetic resonance image (MRI) for predicting the efficacy of neoadjuvant chemotherapy (NACT) in patients with breast cancer (BCa). Methods: This retrospective investigation consisted of 158 patients who were diagnosed with BCa and underwent MRI before NACT, of which 33 patients experienced pathological complete response (pCR) by the postoperative pathological examination. The patients with BCa were divided into the training set (n = 110) and test set (n = 48) randomly. The features were selected by the maximum relevance minimum redundancy (mRMR) and absolute shrinkage and selection operator (LASSO) algorithm in the training set. In return, the radiomics signature was established using machine learning. The predictive score of each patient was calculated using the radiomics signature formula. Finally, the predictive scores and clinical factors were used to perform the multivariate logistic regression and construct the nomogram. Receiver operating characteristics (ROC) analyses were used to assess and validate the diagnostic accuracy of the nomogram in the test set. Lastly, the usefulness of the nomogram was confirmed via decision curve analysis (DCA). Results: The radiomics signature was well-discriminated in the training set [AUC 0.835, specificity 71.32%, and sensitivity 82.61%], and test set (AUC 0.834, specificity 73.21%, and sensitivity 80%). Containing the radiomics signature and hormone status, the radiomics nomogram showed good calibration and discrimination in the training set [AUC 0.888, specificity 79.31%, and sensitivity 86.96%] and test set (AUC 0.879, Chen et al. Radiomics Predicted Efficancy of NACT specificity 82.19%, and sensitivity 83.57%). The decision curve indicated the clinical usefulness of our nomogram. Conclusion: Our radiomics nomogram showed good discrimination in patients with BCa who experience pCR after NACT. The model may aid physicians in predicting how specific patients may respond to BCa treatments in the future.

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Section: Establishment Of An Optimal Radiomics Signature Based On Macmentioning

confidence: 99%

Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

Chen

Shu

et al. 2020

Front. Oncol.

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“…The effectiveness of mRMR-ICA is evaluated using ten benchmark microarray gene expression datasets. Comparing mRMR-ICA to the original ICA and other evolutionary algorithms, experimental results show an improvement in the precision of cancer classification and the number of useful genes [21]. The gene set that DGS chose has demonstrated greater effectiveness in the categorization of cancer.…”

Section: Introductionmentioning

confidence: 99%

A Gene Selection Algorithm for Microarray Cancer Classification Using an improved Particle Swarm Optimization

Nagra,

Khan,

Abubakar

et al. 2024

Preprint

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Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (DNA microarray) facilitates computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This novel algorithm establishes a balance between the exploitation and exploration capabilities of the improved inertia weight adaptive particle swarm optimization. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) algorithm is employed for solution explorations. Each particle in the SIW-APSO increases its position and velocity iteratively through an evolutionary process. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been to identify several genes in the cancer dataset. The classification algorithm contains ELM, K- centroid nearest neighbor (KCNN), and support vector machine (SVM) to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.

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“…Recently, different feature selection methods have been used in developing models for predicting the RNA modification sites. Wang et al (2018) used a minimum redundancy maximum (mRMR) correlation algorithm to select discriminative features from the features encoded based on RNA sequences. Sabooh et al (2018) developed a new computational method pm5CS-Comp-mRMR by also using mRMR for selecting the discriminate features.…”

Section: Introductionmentioning

confidence: 99%

m5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP

et al. 2022

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As one of the most important post-transcriptional modifications of RNA, 5-cytosine-methylation (m5C) is reported to closely relate to many chemical reactions and biological functions in cells. Recently, several computational methods have been proposed for identifying m5C sites. However, the accuracy and efficiency are still not satisfactory. In this study, we proposed a new method, m5Cpred-XS, for predicting m5C sites of H. sapiens, M. musculus, and A. thaliana. First, the powerful SHAP method was used to select the optimal feature subset from seven different kinds of sequence-based features. Second, different machine learning algorithms were used to train the models. The results of five-fold cross-validation indicate that the model based on XGBoost achieved the highest prediction accuracy. Finally, our model was compared with other state-of-the-art models, which indicates that m5Cpred-XS is superior to other methods. Moreover, we deployed the model on a web server that can be accessed through http://m5cpred-xs.zhulab.org.cn/, and m5Cpred-XS is expected to be a useful tool for studying m5C sites.

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Hybrid Feature Selection Algorithm mRMR-ICA for Cancer Classification from Microarray Gene Expression Data

Abstract: The comparison results demonstrate that mRMR-ICA can effectively delete redundant genes to ensure that the algorithm selects fewer informative genes to get better classification results. It also can shorten calculation time and improve efficiency.

Cited by 15 publications

References 0 publications

Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients

A Gene Selection Algorithm for Microarray Cancer Classification Using an improved Particle Swarm Optimization

m5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP

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