A Low-Rank Representation Method Regularized by Dual-Hypergraph Laplacian for Selecting Differentially Expressed Genes

Xu, Xiuxiu; Dai, Lingyun; Kong, Xiang-Zhen; Liu, Jin-Xing

doi:10.1159/000501482

Cited by 2 publications

(2 citation statements)

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“…The relevance score can be obtained from the Genecards database, which is publicly available from http://www.genecards.org/, which is a key indicator for Genecards to evaluate the relationship between genes and diseases, implying that the higher the relevance score of a gene is, the higher the association between the gene and the disease. 19,20 In the tumor classification experiment, multiple criteria such as accuracy (ACC), recall (REC), precision (PRE), and F1- score (F1) are commonly used to evaluate the classification performance. In addition to these four performance measures, the area under the receiver-operating characteristic curve (AUC) 21 is another important performance metric, 22 which reflects the most comprehensive prediction performance.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…TRS is the total sum of the relevance scores of the characteristic genes with respect to the three diseases. The relevance score can be obtained from the Genecards database, which is publicly available from , which is a key indicator for Genecards to evaluate the relationship between genes and diseases, implying that the higher the relevance score of a gene is, the higher the association between the gene and the disease. , …”

Section: Methodsmentioning

confidence: 99%

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Enhancing Characteristic Gene Selection and Tumor Classification by the Robust Laplacian Supervised Discriminative Sparse PCA

Zhang

Liu

et al. 2022

J. Chem. Inf. Model.

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Characteristic gene selection and tumor classification of gene expression data play major roles in genomic research. Due to the characteristics of a small sample size and high dimensionality of gene expression data, it is a common practice to perform dimensionality reduction prior to the use of machine learning-based methods to analyze the expression data. In this context, classical principal component analysis (PCA) and its improved versions have been widely used. Recently, methods based on supervised discriminative sparse PCA have been developed to improve the performance of data dimensionality reduction. However, such methods still have limitations: most of them have not taken into consideration the improvement of robustness to outliers and noise, label information, sparsity, as well as capturing intrinsic geometrical structures in one objective function. To address this drawback, in this study, we propose a novel PCA-based method, known as the robust Laplacian supervised discriminative sparse PCA, termed RLSDSPCA, which enforces the L2,1 norm on the error function and incorporates the graph Laplacian into supervised discriminative sparse PCA. To evaluate the efficacy of the proposed RLSDSPCA, we applied it to the problems of characteristic gene selection and tumor classification problems using gene expression data. The results demonstrate that the proposed RLSDSPCA method, when used in combination with other related methods, can effectively identify new pathogenic genes associated with diseases. In addition, RLSDSPCA has also achieved the best performance compared with the state-of-the-art methods on tumor classification in terms of major performance metrics. The codes and data sets used in the study are freely available at .

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Section: ■ Materials and Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Enhancing Characteristic Gene Selection and Tumor Classification by the Robust Laplacian Supervised Discriminative Sparse PCA

Zhang

Liu

et al. 2022

J. Chem. Inf. Model.

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Identifying potential association on gene-disease network via dual hypergraph regularized least squares

et al. 2021

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Background Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases. Results In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations. Conclusion Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases.

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A Low-Rank Representation Method Regularized by Dual-Hypergraph Laplacian for Selecting Differentially Expressed Genes

Cited by 2 publications

References 47 publications

Enhancing Characteristic Gene Selection and Tumor Classification by the Robust Laplacian Supervised Discriminative Sparse PCA

Enhancing Characteristic Gene Selection and Tumor Classification by the Robust Laplacian Supervised Discriminative Sparse PCA

Identifying potential association on gene-disease network via dual hypergraph regularized least squares

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