Dey Lopamudra, Mukhopadhyay Anirban
Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India.
Department of Computer Science and Engineering, University of Kalyani, Kalyani, India.
J Comput Biol. 2020 May;27(5):755-768. doi: 10.1089/cmb.2019.0171. Epub 2019 Sep 5.
Dengue virus (DENV) is one of the deadly arboviruses, which is primarily transmitted by , and causes dengue infection to the humans. According to WHO, every year around 390 million humans are affected by DENV, of which around 50 million deaths are reported. Knowledge of the various diseases caused by the DENV would greatly encourage to understand the infection mechanism and help to design new antiviral drug discovery. We propose a quasi-clique and quasi-biclique algorithm to classify infection gateway proteins of the human body and possible pathways of DENV leading to various diseases. For this, we have examined three networks, dengue-human protein-protein interaction network, human protein interaction network, and human proteins-disease association network. The prediction result states that DENV may lead to various diseases in the human body, including cancer, asthma, ulcerative colitis, multiple sclerosis, premature birth, and so on. Some of the results have recently been validated experimentally. This study may endow with potential targets for more effective anti-dengue remedial contribution.
登革热病毒（DENV）是致命的虫媒病毒之一，主要通过[此处原文缺失传播媒介]传播，可导致人类感染登革热。据世界卫生组织称，每年约有3.9亿人受到DENV影响，其中报告的死亡人数约为5000万。了解由DENV引起的各种疾病，将极大地有助于理解感染机制，并有助于设计新的抗病毒药物。我们提出了一种准团和准双团算法，用于对人体感染途径蛋白以及DENV导致各种疾病的可能途径进行分类。为此，我们研究了三个网络：登革热-人类蛋白质-蛋白质相互作用网络、人类蛋白质相互作用网络和人类蛋白质-疾病关联网络。预测结果表明，DENV可能导致人体多种疾病，包括癌症、哮喘、溃疡性结肠炎、多发性硬化症、早产等。最近，部分结果已通过实验验证。本研究可能为更有效的抗登革热治疗提供潜在靶点。
