1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Computational prediction of MicroRNA-Cancer relations
Paper ID : 1329-ICB10
Authors:
Mahsa Morattab1, Adel Eghbali2, Shahab Bakhtiari *3, Narjes Takhshid4, Shahrzad Zirak hassan kiadeh5
1Department of Biology,Central Tehran Branch Islamic Azad University,Tehran,Iran
2Department of Medical Laboratory Science, Faculty of paramedical science , Mazandaran university of medical science ,Sari , Iran
3Department of Biological Sciences, University of Kurdistan, Sanandaj, Iran
4School of advanced sciences and technologies, Azad University of Tehran Medical Science,Tehran ,Iran
5Department of Life Science Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Abstract:
Every year over a million people lose their lives because of cancer, which is why scientists are more motivated than ever to carry out numerous researches to understand the fundamental causes of cancer 1. MicroRNAs (miRNAs) are short RNA molecules that bind mRNA, altering their regulation 2. It is feasible to use them as potential diagnostic biomarkers 3 due to their stability and aberrant expression in the pathogenesis of cancer, cardiac, and other diseases 4. They modulate many processes that contribute to different stages of cancers 5. For example, Experimental evidence suggested that MIR21 could be involved in different cancers 6. Many databases provide detailed information regarding these small molecules 7. Link prediction is utilized to estimate hidden relations among existing links based on known topology 8, which is more efficient in a complex network to find missing links or even predict other ones 9.
Furthermore, it promotes discovering the new relations among disease-related miRNAs that could broaden our horizons of the molecular mechanisms of human diseases 10, which could sometimes be challenging in bioinformatics research 11. The accuracy of new algorithms in link prediction has been proved by extensive experiments on both synthetic and real networks 7. In this article, we have created a miRNA-cancer network using four link prediction algorithms, including common neighbors (CN), Preferential attachment (PA), Jaccard (JC), and Adamic and Adar (AA), based on the Human microRNA Disease Database (HMDD v3.2) to predict new miRNA-cancer relations. According to our predictions, hsa-mir-146a-Glioma and hsa-mir-19a-Non-small-cell lung carcinoma (NSCLC), are some of the most probable Hsa-miRNA cancer associations that have not been published in any articles yet, and they could be the best probable candidates for additional laboratory and validation studies.
Keywords:
Key words: miRNA; Link prediction; Cancer; bipartite network; bioinformatics
Status : Paper Accepted (Poster Presentation)