Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes
Differential expression plays an important role in cancer diagnosis and classification. In recent years, many methods have been used to identify differentially expressed genes. However, the recognition rate and reliability of gene selection still need to be improved. In this paper, a novel constrain...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/4216797 |
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| author | Ling-Yun Dai Chun-Mei Feng Jin-Xing Liu Chun-Hou Zheng Jiguo Yu Mi-Xiao Hou |
| author_facet | Ling-Yun Dai Chun-Mei Feng Jin-Xing Liu Chun-Hou Zheng Jiguo Yu Mi-Xiao Hou |
| author_sort | Ling-Yun Dai |
| collection | DOAJ |
| description | Differential expression plays an important role in cancer diagnosis and classification. In recent years, many methods have been used to identify differentially expressed genes. However, the recognition rate and reliability of gene selection still need to be improved. In this paper, a novel constrained method named robust nonnegative matrix factorization via joint graph Laplacian and discriminative information (GLD-RNMF) is proposed for identifying differentially expressed genes, in which manifold learning and the discriminative label information are incorporated into the traditional nonnegative matrix factorization model to train the objective matrix. Specifically, L2,1-norm minimization is enforced on both the error function and the regularization term which is robust to outliers and noise in gene data. Furthermore, the multiplicative update rules and the details of convergence proof are shown for the new model. The experimental results on two publicly available cancer datasets demonstrate that GLD-RNMF is an effective method for identifying differentially expressed genes. |
| format | Article |
| id | doaj-art-b52848fef0fa440aaa5dfc3b463586df |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b52848fef0fa440aaa5dfc3b463586df2025-08-20T02:23:30ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/42167974216797Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed GenesLing-Yun Dai0Chun-Mei Feng1Jin-Xing Liu2Chun-Hou Zheng3Jiguo Yu4Mi-Xiao Hou5School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaSchool of Information Science and Engineering, Qufu Normal University, Rizhao 276826, ChinaDifferential expression plays an important role in cancer diagnosis and classification. In recent years, many methods have been used to identify differentially expressed genes. However, the recognition rate and reliability of gene selection still need to be improved. In this paper, a novel constrained method named robust nonnegative matrix factorization via joint graph Laplacian and discriminative information (GLD-RNMF) is proposed for identifying differentially expressed genes, in which manifold learning and the discriminative label information are incorporated into the traditional nonnegative matrix factorization model to train the objective matrix. Specifically, L2,1-norm minimization is enforced on both the error function and the regularization term which is robust to outliers and noise in gene data. Furthermore, the multiplicative update rules and the details of convergence proof are shown for the new model. The experimental results on two publicly available cancer datasets demonstrate that GLD-RNMF is an effective method for identifying differentially expressed genes.http://dx.doi.org/10.1155/2017/4216797 |
| spellingShingle | Ling-Yun Dai Chun-Mei Feng Jin-Xing Liu Chun-Hou Zheng Jiguo Yu Mi-Xiao Hou Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes Complexity |
| title | Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes |
| title_full | Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes |
| title_fullStr | Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes |
| title_full_unstemmed | Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes |
| title_short | Robust Nonnegative Matrix Factorization via Joint Graph Laplacian and Discriminative Information for Identifying Differentially Expressed Genes |
| title_sort | robust nonnegative matrix factorization via joint graph laplacian and discriminative information for identifying differentially expressed genes |
| url | http://dx.doi.org/10.1155/2017/4216797 |
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