| Autor: |
Chen, Jiazhou, Han, Guoqiang, Xu, Aodan, Cai, Hongmin |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Biomedical Engineering; Apr2020, Vol. 4 Issue 4, p987-998, 12p |
| Abstrakt: |
Objective: The accumulation of large amounts of multidimensional genomic data provides new opportunities to study multilevel biological regulatory associations. Identifying multidimensional regulatory modules (md-modules) from omics data is crucial to provide a comprehensive understanding of the regulatory mechanisms of biological systems. Methods: We develop a multi-graph matching with multiple network constraints (MGMMNC) model to identify the md-modules. The MGMMNC model aims to accurately capture highly relevant md-modules by considering the relationships intra- and inter-multidimensional omics data, including interactions within a network and cycle consistency information. The proposed technique adopts a novel graph-smoothing similarity measurement for the highly contaminated genetic data. Results: The superiority and effectiveness of MGMMNC have been demonstrated by comparative experiments with three state-of-the-art techniques using simulated and cervical cancer data. Conclusion: MGMMNC can accurately and efficiently identify the md-modules that are significantly enriched in gene ontology biological processes and in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Many different level molecules in the same md-module collaboratively regulate the same pathway. Moreover, the md-modules are capable of stratifying patients into subtypes with significant survival differences. Significance: The problem of identifying multidimensional regulatory modules from omics data is formulated as a multi-graph matching problem, and multiple network constraints and cycle consistency information are seamlessly integrated into the matching model. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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