C-KPCA: Custom Kernel PCA for Cancer Classification

Ha, Van-Sang; Nguyen, Ha-Nam

doi:10.1007/978-3-319-41920-6_36

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Generally speaking, dimension reduction method contains data dimension reduction and feature dimension reduction. Data dimension reduction method refers to map high dimensional data into low dimension [36], [37], [38], [39] to get a new data set with lower dimension than before. This method can reduce data • Data Composition.…”

Section: Fig 2 General Data Analysis Procedures For Big Datamentioning

confidence: 99%

An Adaptive Security Data Collection and Composition Recognition method for security measurement over LTE/LTE-A networks

Chen²,

Yan

et al. 2020

Journal of Network and Computer Applications

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Section: Fig 2 General Data Analysis Procedures For Big Datamentioning

confidence: 99%

An Adaptive Security Data Collection and Composition Recognition method for security measurement over LTE/LTE-A networks

Chen²,

Yan

et al. 2020

Journal of Network and Computer Applications

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“…Compared to PCA, the performance of processing nonlinear data in KPCA was improved. Ha et al used the logistic regression method with the features reconstructed by KPCA for cancer classification [10]. With the development of deep learning technology, Autoencoder (AE) was designed to construct lower dimensional representation for integrating the multi-omics data.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based ovarian cancer subtypes identification using multi-omics data

Guo

Wang

et al. 2020

BioData Mining

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Background: Identifying molecular subtypes of ovarian cancer is important. Compared to identify subtypes using single omics data, the multi-omics data analysis can utilize more information. Autoencoder has been widely used to construct lower dimensional representation for multi-omics feature integration. However, learning in the deep architectures in Autoencoder is difficult for achieving satisfied generalization performance. To solve this problem, we proposed a novel deep learning-based framework to robustly identify ovarian cancer subtypes by using denoising Autoencoder. Results: In proposed method, the composite features of multi-omics data in the Cancer Genome Atlas were produced by denoising Autoencoder, and then the generated lowdimensional features were input into k-means for clustering. At last based on the clustering results, we built the light-weighted classification model with L1-penalized logistic regression method. Furthermore, we applied the differential expression analysis and WGCNA analysis to select target genes related to molecular subtypes. We identified 34 biomarkers and 19 KEGG pathways associated with ovarian cancer. Conclusions: The independent test results in three GEO datasets proved the robustness of our model. The literature reviewing show 19 (56%) biomarkers and 8(42.1%) KEGG pathways identified based on the classification subtypes have been proved to be associated with ovarian cancer. The outcomes indicate that our proposed method is feasible and can provide reliable results.

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Survey on Big Data Analysis Algorithms for Network Security Measurement

Chen

Yan

2017

Network and System Security

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C-KPCA: Custom Kernel PCA for Cancer Classification

Cited by 5 publications

References 25 publications

An Adaptive Security Data Collection and Composition Recognition method for security measurement over LTE/LTE-A networks

An Adaptive Security Data Collection and Composition Recognition method for security measurement over LTE/LTE-A networks

Deep learning-based ovarian cancer subtypes identification using multi-omics data

Survey on Big Data Analysis Algorithms for Network Security Measurement

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