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- Publisher Website: 10.1109/TCBB.2018.2848904
- Scopus: eid_2-s2.0-85049092188
- WOS: WOS:000524236800007
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Article: Multi-domain Networks Association for Biological Data Using Block Signed Graph Clustering
Title | Multi-domain Networks Association for Biological Data Using Block Signed Graph Clustering |
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Authors | |
Keywords | Neurons Multi-domain association biological data unsupervised learning spectral clustering Correlation Data integration Laplace equations signed graph Clustering algorithms Clustering methods |
Issue Date | 2018 |
Citation | IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018 How to Cite? |
Abstract | IEEE Multi-domain biological network association and clustering have attracted a lot of attention in biological data integration and understanding. In many problems, different domains may have different cluster structures. Due to growth of data collection from different sources, some domains may be strongly or weakly associated with the other domains. A key challenge is how to determine the degree of association among different domains, and to achieve accurate clustering results by data integration. In this paper, we propose an unsupervised learning approach for multi-domain network association by using block signed graph clustering. In particular, with consistency weights calculation, the proposed algorithm automatically identify domains relevant to each other strongly (or weakly) by assigning them larger (or smaller) weights. This approach not only significantly improve clustering accuracy but also understand multi-domain networks association. In each iteration of the proposed algorithm, we update consistency weights based on cluster structure of each domain, and then make use of different sets of eigenvectors to obtain different cluster structures in each domain. Experimental results on both synthetic data sets and real data sets (neuron activity data and gene expression data) empirically demonstrate the effectiveness of the proposed algorithm in clustering performance and in domain association capability. |
Persistent Identifier | http://hdl.handle.net/10722/276597 |
ISSN | 2021 Impact Factor: 3.702 2020 SCImago Journal Rankings: 0.745 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liu, Ye | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Wu, Stephen | - |
dc.date.accessioned | 2019-09-18T08:34:05Z | - |
dc.date.available | 2019-09-18T08:34:05Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018 | - |
dc.identifier.issn | 1545-5963 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276597 | - |
dc.description.abstract | IEEE Multi-domain biological network association and clustering have attracted a lot of attention in biological data integration and understanding. In many problems, different domains may have different cluster structures. Due to growth of data collection from different sources, some domains may be strongly or weakly associated with the other domains. A key challenge is how to determine the degree of association among different domains, and to achieve accurate clustering results by data integration. In this paper, we propose an unsupervised learning approach for multi-domain network association by using block signed graph clustering. In particular, with consistency weights calculation, the proposed algorithm automatically identify domains relevant to each other strongly (or weakly) by assigning them larger (or smaller) weights. This approach not only significantly improve clustering accuracy but also understand multi-domain networks association. In each iteration of the proposed algorithm, we update consistency weights based on cluster structure of each domain, and then make use of different sets of eigenvectors to obtain different cluster structures in each domain. Experimental results on both synthetic data sets and real data sets (neuron activity data and gene expression data) empirically demonstrate the effectiveness of the proposed algorithm in clustering performance and in domain association capability. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE/ACM Transactions on Computational Biology and Bioinformatics | - |
dc.subject | Neurons | - |
dc.subject | Multi-domain association | - |
dc.subject | biological data | - |
dc.subject | unsupervised learning | - |
dc.subject | spectral clustering | - |
dc.subject | Correlation | - |
dc.subject | Data integration | - |
dc.subject | Laplace equations | - |
dc.subject | signed graph | - |
dc.subject | Clustering algorithms | - |
dc.subject | Clustering methods | - |
dc.title | Multi-domain Networks Association for Biological Data Using Block Signed Graph Clustering | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TCBB.2018.2848904 | - |
dc.identifier.scopus | eid_2-s2.0-85049092188 | - |
dc.identifier.spage | null | - |
dc.identifier.epage | null | - |
dc.identifier.eissn | 1557-9964 | - |
dc.identifier.isi | WOS:000524236800007 | - |
dc.identifier.issnl | 1545-5963 | - |