Bilayer Elastic Net Regression Model for Supervised Spectral-Spatial Hyperspectral Image Classification

Soomro, Bushra Naz; Xiao, Liang; Huang, Lili; Soomro, Shahzad Hyder; Molaei, Mohsen

doi:10.1109/jstars.2016.2559524

Cited by 19 publications

(9 citation statements)

References 33 publications

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“…Elastic network regression, as a combination of ridge regression and lasso regression, can not only reduce the prediction variance but also achieve the purpose of coefficient shrinkage and variable selection [ 20 ]. Therefore, we use elastic net regression to select the key genes.…”

Section: Methodsmentioning

confidence: 99%

Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm

Chen

Qin

2021

BMC Bioinformatics

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Background Predicting the drug response of the cancer diseases through the cellular perturbation signatures under the action of specific compounds is very important in personalized medicine. In the process of testing drug responses to the cancer, traditional experimental methods have been greatly hampered by the cost and sample size. At present, the public availability of large amounts of gene expression data makes it a challenging task to use machine learning methods to predict the drug sensitivity. Results In this study, we introduced the WRFEN-XGBoost cell viability prediction algorithm based on LINCS-L1000 cell signatures. We integrated the LINCS-L1000, CTRP and Achilles datasets and adopted a weighted fusion algorithm based on random forest and elastic net for key gene selection. Then the FEBPSO algorithm was introduced into XGBoost learning algorithm to predict the cell viability induced by the drugs. The proposed method was compared with some new methods, and it was found that our model achieved good results with 0.83 Pearson correlation. At the same time, we completed the drug sensitivity validation on the NCI60 and CCLE datasets, which further demonstrated the effectiveness of our method. Conclusions The results showed that our method was conducive to the elucidation of disease mechanisms and the exploration of new therapies, which greatly promoted the progress of clinical medicine.

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Section: Methodsmentioning

confidence: 99%

Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm

Chen

Qin

2021

BMC Bioinformatics

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“…Elastic network regression, as a combination of ridge regression and lasso regression, can not only reduce the prediction variance but also achieve the purpose of coefficient shrinkage and variable selection [20]. Therefore, we use elastic net regres- sion to select the key genes.…”

Section: Feature Extraction Based On Random Forest and Elastic Netmentioning

confidence: 99%

Drug-Induced Cell Viability Prediction from LINCS-L1000 through WRFEN-XGBoost Algorithm

Chen

Qin

2020

Preprint

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Background: Predicting the drug response of the cancer diseases through the cellular perturbation signatures under the action of specific compounds is very important in personalized medicine. In the process of testing drug responses to the cancer, traditional experimental methods have been greatly hampered by the cost and sample size. At present, the public availability of large amounts of gene expression data makes it a challenging task to use machine learning methods to predict the drug sensitivity. Results: In this study, we introduced the WRFEN-XGBoost cell viability prediction algorithm based on LINCS-L1000 cell signatures. We integrated the LINCS-L1000, CTRP and Achilles datasets and adopted a weighted fusion algorithm based on random forest and elastic net for key gene selection. Then the FEBPSO algorithm was introduced into XGBoost learning algorithm to predict the cell viability induced by the drugs. The proposed method was compared with some new methods, and it was found that our model achieved good results with 0.83 Pearson correlation. At the same time, we completed the drug sensitivity validation on the NCI60 and CCLE datasets, which further demonstrated the effectiveness of our method. Conclusions: The results showed that our method was conducive to the elucidation of disease mechanisms and the exploration of new therapies, which greatly promoted the progress of clinical medicine.

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“…The overall accuracy, producer accuracy, user accuracy, commission, omission, and kappa coefficient were used as the assessment parameters during these comparisons. [14][15][16][17][18][19] 3 Results…”

Section: Accuracy Analysis and Comparisonmentioning

confidence: 99%

Accuracy assessment model for classification result of remote sensing image based on spatial sampling

Huang

Sun

et al. 2017

J. Appl. Remote Sens

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Abstract. The classification accuracy of a remote sensing image should be assessed before the classification result is used for scientific investigation and policy decision. We proposed an accuracy assessment model based on spatial sampling to reflect region sensitivity of a remote sensing image. The proposed model aims to solve the following problems: (1) what sampling size should be selected for accuracy assessment; (2) where sample points should be distributed in a region; and (3) how to analyze the result of accuracy assessment. This assessment model was proposed based on gray-level co-occurrence matrix (GLCM) and considered both sampling size calculation and sample points distribution during the assessment. The overall accuracy and kappa coefficient derived from this model were very close to the true value derived from the total assessment, suggesting that the assessment accuracy of the model is close to that of total assessment. Compared with the percent sampling model, the model could quantify the relationship between GLCM-correlation parameter and sample size, thereby allowing producer and user to determine sample size according to spatial uniformity and heterogeneity. Compared with the random sampling model, the model could ensure that the sample points are uniformly distributed in the spatial region and proportionally distributed in different types of land cover. Taken together, the proposed model is suitable for the accuracy assessment of the classification result of a remote sensing image.

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Bilayer Elastic Net Regression Model for Supervised Spectral-Spatial Hyperspectral Image Classification

Cited by 19 publications

References 33 publications

Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm

Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm

Drug-Induced Cell Viability Prediction from LINCS-L1000 through WRFEN-XGBoost Algorithm

Accuracy assessment model for classification result of remote sensing image based on spatial sampling

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