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• W3015655681 abstract "Abstract Cancer is the major burden of diseases around the world. The incidence and mortality rate of cancers is mounting up with the passage of days. Breast cancer is the most demoralizing cause of death, where both diseases interlocked with each other due to some genetic, biological and behavioral motives. The molecular mechanism of breast cancer through which they crop up and manifest together remains questionable. The genetic basis of protein-protein interactions and gene networks has elucidated a group of gene regulatory systems in Breast cancer. Thus, the extraction of all genomic and proteomic data has enabled unprecedented views of gene-protein co-expression, co-regulation, and interactions in the biological system. This study explored the biological system to develop a gene-disease interaction model by implementing the extracted genomic and proteomic data of Breast cancer. The disease-specific and correlated genes were pulled out and their cabling studied by PPI, disease pathway and drug-disease interaction data to articulate their role in disease development. By analyzing mined genes that are related to breast cancer, a network model is also proposed. Exploration of all the correlated genes, Hub and common genes have given some promising pieces of evidence surrounding the genetic networking models. The result of this prospective study disclosed breast cancer mediated crosslinking or possible metastatic relation on a genetic basis. Moreover, other diseases like prostate, colorectal and ovarian cancers are at the same risk and might count into consideration. The finding provides a narrative broad approach for understanding the genetic basis of these fatal diseases by the pathway analysis with gene regulatory network evaluation." @default.
• W3015655681 created "2020-04-17" @default.
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• W3015655681 date "2020-04-10" @default.
• W3015655681 modified "2023-09-27" @default.
• W3015655681 title "Combinatorial analysis of gene regulatory network reveals the causal genetic basis of breast cancer and gene-specific personalized drug treatments" @default.
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• W3015655681 doi "https://doi.org/10.1101/2020.04.10.035998" @default.
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