Abstract
In this paper we use the genetic network (GN) structural analysis for identifying the plausible main genes in breast cancer. The interaction and mutual information among them could detemine the patterns of genesis in brest cancer primary tumors. The analyzed data is from biopsies in the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO). ARACNE algorithm is applied to find out the correlation matrix of gene expressions in biopsies, thereafter the GN is constructed. The each node-gene and the weighted links between them allow for genes importance in the GN. The gene ranking is obtained with both the gene-node degree and the mutual information or correlation values between each other gene. To get reliable conclusions, the single GN analysis is hundred times replicated and averaging the results. This way, to find out patterns of shared expression through genes in breast cancer primary tumor, an initial data statistical analysis is practiced.
Thanks to Consejo Nacional de Ciencia y Tecnología de México, CONACyT: Project A1-S-20037, Matías Alvarado principal investigator; and the Moises León Pinedas’ Master Scholarship CVU 1144833.
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Notes
- 1.
NetworkX is a Python library [18], quite popular for complex networks studies.
- 2.
The used device is with RAM 16 GB, processor 11th Gen Intel®Core\(^\textrm{TM}\) i7-11800H @ 2.30GHz x 16, disk 1.0 TB, OS Ubuntu 22.04 LTS of 64 bits.
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Acknowledgement
Thanks to Consejo Nacional de Ciencia y Tecnología de México: Project A1-S-20037, Matías Alvarado principal investigator; and Moisés León Pinedas’ Master Scholarship No. CVU: 1144833.
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León, M., Alvarado, M. (2023). Patterns in Genesis of Breast Cancer Tumor. In: Rodríguez-González, A.Y., Pérez-Espinosa, H., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2023. Lecture Notes in Computer Science, vol 13902. Springer, Cham. https://doi.org/10.1007/978-3-031-33783-3_18
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