Efficient techniques on retrieving bio-information for active U-healthcare

초록

Recently, active prevention healthcares are needed for potential patients to be suffered in the future as the forecasted diseases inherited from ancestors. We call active U-healthcare, for providing active, periodic, and continuous medical treatments depending on inherited heterogeneous states in DNAs of patients, such as diabetes, heart diseases, and female diseases. However, the bottleneck of the aggressive active U-healthcare is memory overhead in DNA sequence analysis of each patient since the sequences of DNAs have massive volume. Thus, the efficient retrieve of the many disease patterns in originally recorded on DNAs of potential patients is a major problem. This paper focuses on a novel method for efficient retrieving of disease patterns using a suffix tree in memory. The suffix tree is widely used in the similarity search for sequences consisting of limited characters. It is efficient when the occurrence frequency of a common prefix is high. Since in-memory suffix tree construction algorithms do not scale up, a large-scale disk-based suffix tree construction algorithm, TRELLIS, has been proposed recently. However, the algorithm requires a large amount of memory, disk space, and disk I/Os in order to merge sub-trees having a common prefix. In this paper, we propose a new non-merging method, called NST. The experimental results show that NST constructs an index using less memory than TRELLIS.

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