A Novel Gene Selection Method Based on Sparse Representation and Max-Relevance and Min-Redundancy

Chen, Min; He, Xiaoming; Duan, ShaoBin; Deng, Yingwei

doi:10.2174/1386207320666170126114051

Cited by 4 publications

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“…Then, the least absolute shrinkage and selection operator (LASSO) algorithm was employed to identify the most predictive features according to their associations with the EGFR mutation and subtypes with 5-fold cross-validation using the glmnet package in R language v3.6 (available from URL: https://www.r-project.org ). The extracted features were further picked using the Max-Relevance and Min-Redundancy (mRMR) ( 29 ). Finally, the remained features were treated with the logistic regression ( 30 ) with Akaike Information Criterion (AIC) as the stopping rule.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma

et al. 2023

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PurposeTo investigate the use of multiparameter MRI-based radiomics in the in-depth prediction of epidermal growth factor receptor (EGFR) mutation and subtypes based on the spinal metastasis in patients with primary lung adenocarcinoma.MethodsA primary cohort was conducted with 257 patients who pathologically confirmed spinal bone metastasis from the first center between Feb. 2016 and Oct. 2020. An external cohort was developed with 42 patients from the second center between Apr. 2017 and Jun. 2021. All patients underwent sagittal T1-weighted imaging (T1W) and sagittal fat-suppressed T2-weight imaging (T2FS) MRI imaging. Radiomics features were extracted and selected to build radiomics signatures (RSs). Machine learning classify with 5-fold cross-validation were used to establish radiomics models for predicting the EGFR mutation and subtypes. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors. Nomogram models were developed integrating the RSs and important clinical factors.ResultsThe RSs derived from T1W showed better performance for predicting the EGFR mutation and subtypes compared with those from T2FS in terms of AUC, accuracy and specificity. The nomogram models integrating RSs from combination of the two MRI sequences and important clinical factors achieved the best prediction capabilities in the training (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.829 vs. 0.885 vs.0.919), internal validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.760 vs. 0.777 vs.0.811), external validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.780 vs. 0.846 vs.0.818). DCA curves indicated potential clinical values of the radiomics models.ConclusionsThis study indicated potentials of multi-parametric MRI-based radiomics to assess the EGFR mutation and subtypes. The proposed clinical-radiomics nomogram models can be considered as non-invasive tools to assist clinicians in making individual treatment plans.

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Section: Methodsmentioning

confidence: 99%

Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma

et al. 2023

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“…Typical linear feature extraction algorithms include sparse principal component analysis (PCA) ( Min et al, 2018 ; Islam et al, 2020 ), independent component analysis ( Moysés et al, 2017 ), and LDA. Nonlinear transformation methods primarily include neural networks, kernel methods ( Qi et al, 2021b ), manifold learning ( Shen et al, 2017 ), sparse representation ( Min et al, 2017 ), and matrix factorization methods ( Wang et al, 2017 ; Yang et al, 2017 ; Yang and Hu, 2017 ; McCall et al, 2019 ). With the continuous development of machine learning and data mining, new feature extraction methods continue to arise.…”

Section: Application Of Sparse Representation In Bioinformaticsmentioning

confidence: 99%

Application of Sparse Representation in Bioinformatics

Han

Wang²,

Guo

et al. 2021

Front. Genet.

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Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority. Researchers have not only extended the sparse representation of a signal to image presentation, but also applied the sparsity of vectors to that of matrices. Moreover, sparse representation has been applied to pattern recognition with good results. Because of its multiple advantages, such as insensitivity to noise, strong robustness, less sensitivity to selected features, and no “overfitting” phenomenon, the application of sparse representation in bioinformatics should be studied further. This article reviews the development of sparse representation, and explains its applications in bioinformatics, namely the use of low-rank representation matrices to identify and study cancer molecules, low-rank sparse representations to analyze and process gene expression profiles, and an introduction to related cancers and gene expression profile database.

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“…Machine learning methods have also entered the eld of bioinformatics research. [40][41][42] Support vector machines (SVMs) were used by Jiang et al, 43 Xu et al, 44 Zeng et al 45 and Wang et al, 46 a logistic model tree was used by Wang et al, 47 and a decision tree was used by Zhao et al; 48 these are excellent classication tools with global optimality and better generalization abilities to predict potential disease-related candidate miRNAs, but such methods require known negative sample information related to disease-related miRNAs that is difficult to obtain. In order to solve the problem of negative sample acquisition, Chen et al 49 used a regularized least squares approach to optimize similarity networks of miRNAs and diseases, respectively, and the nal miRNA-disease associations were linear weightings of miRNA similarity scores and disease similarity scores.…”

Section: Introductionmentioning

confidence: 99%

LSGSP: a novel miRNA–disease association prediction model using a Laplacian score of the graphs and space projection federated method

et al. 2019

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A Novel Gene Selection Method Based on Sparse Representation and Max-Relevance and Min-Redundancy

Abstract: The effectiveness and stability of our method have been proven through various experiments, which means that our method has practical significance.

Cited by 4 publications

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Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma

Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma

Application of Sparse Representation in Bioinformatics

LSGSP: a novel miRNA–disease association prediction model using a Laplacian score of the graphs and space projection federated method

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