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Predicting the effect of LncRNAs on different types of cancers in bipartite networks using link prediction | ||
| Journal of Epigenetics | ||
| دوره 4، شماره 1، آذر 2023، صفحه 45-40 اصل مقاله (621.23 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22111/jep.2023.45307.1054 | ||
| نویسندگان | ||
| Bahar Ataei* 1؛ Shahrzad benvidi2؛ Parnaz Soori2؛ shahab Bakhtiari3 | ||
| 1Department of Genetics, Faculty of Basic Science, Shahrekord University, Shahrekord, Iran, | ||
| 2Department of Biology, Faculty of Basic Science, Islamic Republic Azad University, Tehran, Iran. | ||
| 3Department of Biological Science, University of Kurdistan, Sanandaj, Iran | ||
| چکیده | ||
| Many complex systems such as the Internet, the World Wide Web, the brain, and the causes of diseases can be described by networks with nodes representing agents and links representing relationships or interactions between nodes. Despite these systems seeming utterly different at first glance, they are all made up of interacting parts. Individual objects in this type of system are not isolated but connected through links or relationships. Long non-coding RNAs (LncRNAs), one of the factors related to many diseases, are specific genes in the human genome that control many different biological processes. lncRNAs have been shown to regulate cancer development and occurrence. We used link prediction bioinformatics tools to identify lncRNAs affecting various types of cancers. To achieve this goal, two-part networks were used using CN (Common Neighbors), AA (Adamic/Adar), PA (Preferential Attachment), and JC (Jaccard's Coefficient) algorithms. The results indicated that all the obtained lncRNAs with a high score had been reviewed in other research articles, which was a sign of the correctness of our algorithms. 4.5% of the obtained results lacked scientific and research studies, all with a high score for future studies and included lncRNA H19 and SPRY4-IT1. In addition, in one case with a high score and first position in the Excel file obtained using the Jaccard coefficient algorithm, lncRNA with the symbol AC09510.3 was associated with adenocarcinoma. Still, this possible link has not been investigated in any article yet. Conclusion: The current study may identify novel lncRNAs implicated in Adenocarcinoma, vulva squamous cell carcinoma, basal cell carcinoma, gestational choriocarcinoma, chromophobe renal cell carcinoma, brain cancer, pancreatic cancer, hepatocellular carcinoma, prostate cancer, embryonal cancer, germ cell cancer, and choriocarcinoma; however, further research is required to determine the potential functions of this lncRNAs in cancers mentioned above. | ||
| کلیدواژه‌ها | ||
| Cancer؛ LncRNA؛ Link prediction؛ AA؛ JC؛ PA؛ CN | ||
| مراجع | ||
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