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Biclustering-based association rule mining approach for predicting cancer-associated protein interactions.
IET Systems Biology ( IF 2.3 ) Pub Date : 2019-10-01 , DOI: 10.1049/iet-syb.2019.0045
Lopamudra Dey 1 , Anirban Mukhopadhyay 2
Affiliation  

Protein-protein interactions (PPIs) have been widely used to understand different biological processes and cellular functions associated with several diseases like cancer. Although some cancer-related protein interaction databases are available, lack of experimental data and conflicting PPI data among different available databases have slowed down the cancer research. Therefore, in this study, the authors have focused on various proteins that are directly related to different types of cancer disease. They have prepared a PPI database between cancer-associated proteins with the rest of the human proteins. They have also incorporated the annotation type and direction of each interaction. Subsequently, a biclustering-based association rule mining algorithm is applied to predict new interactions with type and direction. This study shows the prediction power of association rule mining algorithm over the traditional classifier model without choosing a negative data set. The time complexity of the biclustering-based association rule mining is also analysed and compared to traditional association rule mining. The authors are able to discover 38 new PPIs which are not present in the cancer database. The biological relevance of these newly predicted interactions is analysed by published literature. Recognition of such interactions may accelerate a way of developing new drugs to prevent different cancer-related diseases.

中文翻译:

用于预测癌症相关蛋白相互作用的基于双聚类的关联规则挖掘方法。

蛋白质-蛋白质相互作用 (PPI) 已被广泛用于了解与癌症等多种疾病相关的不同生物过程和细胞功能。尽管有一些与癌症相关的蛋白质相互作用数据库可用,但不同可用数据库之间缺乏实验数据和相互矛盾的 PPI 数据已经减缓了癌症研究。因此,在这项研究中,作者将重点放在与不同类型癌症疾病直接相关的各种蛋白质上。他们已经在癌症相关蛋白质与其他人类蛋白质之间建立了一个 PPI 数据库。他们还结合了每个交互的注释类型和方向。随后,应用基于双聚类的关联规则挖掘算法来预测与类型和方向的新交互。本研究显示了关联规则挖掘算法在不选择负数据集的情况下对传统分类器模型的预测能力。还分析了基于双聚类的关联规则挖掘的时间复杂度,并与传统的关联规则挖掘进行了比较。作者能够发现癌症数据库中不存在的 38 种新的 PPI。这些新预测的相互作用的生物学相关性通过已发表的文献进行分析。认识到这种相互作用可能会加速开发新药以预防不同的癌症相关疾病。还分析了基于双聚类的关联规则挖掘的时间复杂度,并与传统的关联规则挖掘进行了比较。作者能够发现癌症数据库中不存在的 38 种新的 PPI。这些新预测的相互作用的生物学相关性通过已发表的文献进行分析。认识到这种相互作用可能会加速开发新药以预防不同的癌症相关疾病。还分析了基于双聚类的关联规则挖掘的时间复杂度,并与传统的关联规则挖掘进行了比较。作者能够发现癌症数据库中不存在的 38 种新的 PPI。这些新预测的相互作用的生物学相关性通过已发表的文献进行分析。认识到这种相互作用可能会加速开发新药以预防不同的癌症相关疾病。
更新日期:2019-11-01
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