Identification of MicroRNA Regulatory Modules by Clustering MicroRNA-Target Interactions
Identification of microRNA regulatory modules can help decipher microRNA synergistic regulatory mechanism in the development and progression of complex diseases, especially cancers. Experimentally validated microRNA-target interactions provide strong direct evidence for the analysis of microRNA regu...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9172068/ |
| _version_ | 1818616724083179520 |
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| author | Yi Yang Xuting Wan |
| author_facet | Yi Yang Xuting Wan |
| author_sort | Yi Yang |
| collection | DOAJ |
| description | Identification of microRNA regulatory modules can help decipher microRNA synergistic regulatory mechanism in the development and progression of complex diseases, especially cancers. Experimentally validated microRNA-target interactions provide strong direct evidence for the analysis of microRNA regulatory functions. We here developed a novel computational framework named CMIN to identify microRNA regulatory modules by performing link clustering on such experimentally verified microRNA-target interactions. CMIN runs in two main steps: it first utilizes convolutional autoencoders to extract high-level microRNA-target interaction features from the expression profile data, and then applied affinity propagation clustering algorithm to interaction feature to obtain overlapping microRNA-target clusters. Clusters with significant synergy correlations are considered as microRNA regulatory modules. We tested the proposed framework and other three existing methods on three types of cancer data sets from TCGA (The Cancer Genome Atlas). The results showed that the microRNA regulatory modules detected by CMIN exhibit stronger topological correlation and more functional enrichment compared with other methods. Availability: The supplementary files of CMIN are available at https://github.com/snryou/CMIN. |
| first_indexed | 2024-12-16T16:54:21Z |
| format | Article |
| id | doaj.art-28b0b474a08847c0929e11d2086b0f27 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:54:21Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-28b0b474a08847c0929e11d2086b0f272022-12-21T22:23:56ZengIEEEIEEE Access2169-35362020-01-01815413315414210.1109/ACCESS.2020.30181059172068Identification of MicroRNA Regulatory Modules by Clustering MicroRNA-Target InteractionsYi Yang0https://orcid.org/0000-0003-2621-8027Xuting Wan1https://orcid.org/0000-0002-4650-9393College of Information Science and Engineering, Hunan Women’s University, Changsha, ChinaCollege of Information Science and Engineering, Hunan Women’s University, Changsha, ChinaIdentification of microRNA regulatory modules can help decipher microRNA synergistic regulatory mechanism in the development and progression of complex diseases, especially cancers. Experimentally validated microRNA-target interactions provide strong direct evidence for the analysis of microRNA regulatory functions. We here developed a novel computational framework named CMIN to identify microRNA regulatory modules by performing link clustering on such experimentally verified microRNA-target interactions. CMIN runs in two main steps: it first utilizes convolutional autoencoders to extract high-level microRNA-target interaction features from the expression profile data, and then applied affinity propagation clustering algorithm to interaction feature to obtain overlapping microRNA-target clusters. Clusters with significant synergy correlations are considered as microRNA regulatory modules. We tested the proposed framework and other three existing methods on three types of cancer data sets from TCGA (The Cancer Genome Atlas). The results showed that the microRNA regulatory modules detected by CMIN exhibit stronger topological correlation and more functional enrichment compared with other methods. Availability: The supplementary files of CMIN are available at https://github.com/snryou/CMIN.https://ieeexplore.ieee.org/document/9172068/MicroRNA regulatory moduleMicroRNA-target interactionconvolutional autoencoderaffinity propagationlink clustering |
| spellingShingle | Yi Yang Xuting Wan Identification of MicroRNA Regulatory Modules by Clustering MicroRNA-Target Interactions IEEE Access MicroRNA regulatory module MicroRNA-target interaction convolutional autoencoder affinity propagation link clustering |
| title | Identification of MicroRNA Regulatory Modules by Clustering MicroRNA-Target Interactions |
| title_full | Identification of MicroRNA Regulatory Modules by Clustering MicroRNA-Target Interactions |
| title_fullStr | Identification of MicroRNA Regulatory Modules by Clustering MicroRNA-Target Interactions |
| title_full_unstemmed | Identification of MicroRNA Regulatory Modules by Clustering MicroRNA-Target Interactions |
| title_short | Identification of MicroRNA Regulatory Modules by Clustering MicroRNA-Target Interactions |
| title_sort | identification of microrna regulatory modules by clustering microrna target interactions |
| topic | MicroRNA regulatory module MicroRNA-target interaction convolutional autoencoder affinity propagation link clustering |
| url | https://ieeexplore.ieee.org/document/9172068/ |
| work_keys_str_mv | AT yiyang identificationofmicrornaregulatorymodulesbyclusteringmicrornatargetinteractions AT xutingwan identificationofmicrornaregulatorymodulesbyclusteringmicrornatargetinteractions |