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access icon free Biclustering-based association rule mining approach for predicting cancer-associated protein interactions

  • Author(s): Lopamudra Dey 1  and  Anirban Mukhopadhyay 2
    • View affiliations
  • Source: Volume 13, Issue 5, October 2019, p. 234 – 242
    DOI:  10.1049/iet-syb.2019.0045 , Print ISSN 1751-8849, Online ISSN 1751-8857
© The Institution of Engineering and Technology
Received 19/03/2019, Accepted 18/06/2019, Revised 03/06/2019, Published 25/06/2019

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.

Inspec keywords: medical computing; data mining; cancer; proteins; pattern clustering; genetics

Other keywords: protein–protein interactions; cancer research; cancer-related protein interaction databases; cancer-associated protein interactions; cancer-related diseases; biological processes; PPI database; cancer-associated proteins; annotation type; negative data set; human proteins; biclustering-based association rule mining approach; cancer disease

Subjects: Knowledge engineering techniques; Data handling techniques; Biology and medical computing

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