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WeCoMXP: Weighted Connectivity Measure Integrating Co-Methylation, Co-Expression and Protein-Protein Interactions for Gene-Module Detection
- Source :
- IEEE/ACM transactions on computational biology and bioinformatics. 17(2)
- Publication Year :
- 2018
-
Abstract
- The identification of modules (groups of several tightly interconnected genes) in gene interaction network is an essential task for better understanding of the architecture of the whole network. In this article, we develop a novel weighted connectivity measure integrating co-methylation, co-expression, and protein-protein interactions (called $WeCoMXP$ W e C o M X P ) to detect gene-modules for multi-omics dataset. The proposed measure goes beyond the fundamental degree centrality measure through considering some formulation of higher-order connections. Thereafter, we apply the average linkage clustering method using the corresponding dissimilarity (distance) values of $WeCoMXP$ W e C o M X P scores, and utilize a dynamic tree cut method for identifying some gene-modules. We validate the modules through literature search, KEGG pathway, and gene-ontology analyses on the genes representing the modules. Furthermore, the top 10 TFs/miRNAs that are connected with the maximum number of gene-modules and that regulate/target the maximum number of genes from these connected gene-modules, are identified. Moreover, our proposed method provides a better performance than the existing methods in terms of several cluster-validity indices in maximum times.
- Subjects :
- Computer science
0206 medical engineering
02 engineering and technology
Measure (mathematics)
Protein–protein interaction
Correlation
Databases, Genetic
Genetics
Humans
Gene Regulatory Networks
Protein Interaction Maps
business.industry
Applied Mathematics
Computational Biology
Pattern recognition
Sarcoma
Methylation
DNA Methylation
Tree (graph theory)
Expression (mathematics)
Identification (information)
MicroRNAs
Artificial intelligence
Centrality
business
Transcriptome
020602 bioinformatics
Biotechnology
Transcription Factors
Subjects
Details
- ISSN :
- 15579964
- Volume :
- 17
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM transactions on computational biology and bioinformatics
- Accession number :
- edsair.doi.dedup.....900b32cbaa39f2fbad12df338a7d2fd1