American Journal of Bioinformatics Research
p-ISSN: 2167-6992 e-ISSN: 2167-6976
2023; 12(1): 11-19
doi:10.5923/j.bioinformatics.20231201.02
Received: Sep. 30, 2023; Accepted: Oct. 26, 2023; Published: Dec. 25, 2023
Emmanuel M. Ayodele
Department of Computer Science, Igbajo Polytechnic, Igbajo, Osun State, Nigeria
Correspondence to: Emmanuel M. Ayodele, Department of Computer Science, Igbajo Polytechnic, Igbajo, Osun State, Nigeria.
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Copyright © 2023 The Author(s). Published by Scientific & Academic Publishing.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
A gene network produced by an interactome network, taking a Protein – Protein Interaction as our choice of use, genes that causes similar diseases lie close to each other in a network and produces a high level of interaction among each other. There is a difficulty describing cancer disease with only one gene but this article is interested in introducing the major genes that are involved in causing or suppressing cancer in human through the network approach. This difficulty comes out of the fact that proteins interacts with each others within a cell and those interactions can be represented through a network defined as an abstract representation of nodes and vertices. A human interactome and expression data network was built and the created network was filtered using the fold change variable and new sub networks was created; and finally the genes that stand out in these networks were identified. This article considers the effect of cell fold change on the visual characteristics of the human interactome and acute myeloid and leukaemia cells expression data network. The fold change is a measure of the difference between a normal cell and a cancerous cell. To achieve our objectives, we followed the following methodology: First, we selected the protein-protein network of interest, i.e. the human interactome. Next, we obtained expression data and then layer the gene expression data on the interactome network data using the Cytoscape tool.
Keywords: Human Interactome, Fold Change, Myeloid, Leukemia
Cite this paper: Emmanuel M. Ayodele, Cancer Gene Identification Using Network Approach, American Journal of Bioinformatics Research, Vol. 12 No. 1, 2023, pp. 11-19. doi: 10.5923/j.bioinformatics.20231201.02.
Figure 1. The Cytoscape Open Architecture. (Source: Shannon P et al. 2003) |
Figure 2. The Node Degree Distribution of the Human Interactome and acute myeloid and leukaemia cells expression data network |
Figure 3. The average clustering coefficient distribution of the Human Interactome and acute myeloid and leukaemia cells expression data network |
Figure 4. The Human Interactome and acute myeloid and leukaemia cells expression data network with the prefuse force directed layout applied in Cytoscape |
Figure 5. The acute myeloid and leukaemia cells Gene Expression data |
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Figure 6. The down regulated network 0 |
Figure 7. The down regulated network: 0.5 |
Figure 8. The upregulated network: 2< fold change <3.9 |
Figure 9. The RUNX2 gene |
Figure 10. The TCF 12 gene |
Figure 11. The EGR 1 gene |