• KIPS Transactions on Software and Data Engineering
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• v.7 no.2
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• pp.51-62
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• 2018

High utility sequential pattern (HUSP) mining has been considered as an important research topic in data mining. Although some algorithms have been proposed for this topic, they incur the problem of producing a large search space for HUSPs. The tighter utility upper bound of a sequence can prune more unpromising patterns early in the search space. In this paper, we propose a sequence expected utility (SEU) as a new utility upper bound of each sequence, which is the maximum expected utility of a sequence and all its descendant sequences. A sequence utility list for each pattern is used as a new data structure to maintain essential information for mining HUSPs. We devise an algorithm, high sequence utility list-span (HSUL-Span), to identify HUSPs by employing SEU. Experimental results on both synthetic and real datasets from different domains show that HSUL-Span generates considerably less candidate patterns and outperforms other algorithms in terms of execution time.

• Genomics & Informatics
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• v.7 no.4
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• pp.217-219
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• 2009

The primary clue for locating protein-coding regions is the open reading frame and the determination of ORFs (Open Reading Frames) is the first step toward the gene prediction, especially for prokaryotes. In this respect, we have developed a web-based ORF search tool called ORF Miner. The ORF Miner is a graphical analysis utility which determines all possible open reading frames of a selectable minimum size in an input sequence. This tool identifies all open reading frames using alternative genetic codes as well as the standard one and reports a list of ORFs with corresponding deduced amino acid sequences. The ORF Miner can be employed for sequence annotation and give a crucial clue to determination of actual protein-coding regions.

• BMB Reports
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• v.37 no.1
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• pp.122-132
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• 2004

The rapid development and characterization of the mouse genome sequence, coupled with comparative sequence analysis of human, has been paralleled by a reinforced enthusiasm for mouse functional genomics. The way to uncover the in vivo function of genes is to analyze the phenotypes of the mutant animals. From this standpoint, the mouse is a suitable and valuable model organism in the studies of functional genomics. Therefore, there have been enormous efforts to enrich the list of the mutant mice. Such a trend emphasizes the random mutagenesis, including ENU mutagenesis and gene-trap mutagenesis, to obtain a large stock of mutant mice. However, since various mutant alleles are needed to precisely characterize the role of a gene in vivo, mutations should be designed. The simplicity and utility of transgenic technology can satisfy this demand. The combination of RNA interference with transgenic technology will provide more opportunities for researchers. Nevertheless, gene targeting can solely define the in vivo function of a gene without a doubt. Thus, transgenesis and gene targeting will be the major strategies in the field of functional genomics.