Discovering Sequence Association Rules for Protein Structure Prediction 


Vol. 8,  No. 5, pp. 553-560, Oct.  2001
10.3745/KIPSTD.2001.8.5.553


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  Abstract

Bioinformatics is a discipline to support biological experiment projects by storing, managing and analyzing data arising from genome research. It can also lead the experimental design for genomic function prediction and regulation. Among various approaches of the genome research, the proteomics have been drawing increasing attention since it deals with the final product of genomes, i.e., proteins, directly. This paper proposes a data mining technique to predict the structural characteristic of a given protein group, one of dominant factors of the functions of them. After explains associations among amino acid subsequences in the primary structures of proteins, which can provide important clues for determining secondary or tertiary structures of them, it defines a sequence association rule to represent the inter-subsequence associations. It also provides support and confidence measures, newly designed to evaluate the usefulness of sequence association rules. After it proposes a method to discover useful sequence association rules from a given protein group, it evaluates the performance of the proposed method with protein sequence data from the SWISS-PROT protein database.

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[IEEE Style]

J. J. Kim, D. H. Lee, Y. J. Baek, "Discovering Sequence Association Rules for Protein Structure Prediction," The KIPS Transactions:PartD, vol. 8, no. 5, pp. 553-560, 2001. DOI: 10.3745/KIPSTD.2001.8.5.553.

[ACM Style]

Jung Ja Kim, Do Heon Lee, and Yun Ju Baek. 2001. Discovering Sequence Association Rules for Protein Structure Prediction. The KIPS Transactions:PartD, 8, 5, (2001), 553-560. DOI: 10.3745/KIPSTD.2001.8.5.553.

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