文章基本信息

标题：A Theoretical Approach for Augmenting Association Rule Mining to Predict Protein-Protein Interaction
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作者：Sony Snigdha Sahoo ; Tripti Swarnkar
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2011
卷号：2
期号：2
页码：776-779
出版社：TechScience Publications
摘要：Background:Every biological process occurring within the living body involves the formation of protein complexes. Interactions between proteins are an important protein feature. Therefore, determining protein interaction has become one of the most significant problems in the post genomic era. Methodology:For effectively determining the interactions occurring among proteins computational approaches like association rule mining could be used. But, only support and confidence measures used with association rule mining can be insufficient at filtering out interesting rules, because it fails in implying the kind of association between given datasets. Correlation measure when used along with association mining could augment the support-confidence framework by deciding whether the association is positive or negative. Conclusion:In this study, we have presented a comparison between association rule mining and correlation in an attempt to indicate the ways in which correlation can augment the support-confidence framework.
关键词：Protein interaction; computational approach;association rule mining; correlation; support-confidence;framework.