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Maebashi, Japan

Maebashi Institute of Technology is a public university at Maebashi, Gunma, Japan. The predecessor of the school was founded in 1952 and was chartered as a university in 1997. Wikipedia.

Sato M.,Maebashi Institute of Technology
Procedia Computer Science | Year: 2012

Promoters are key control regions for the transcription regulation of genes, usually lying upstream of the genes they control. Promoter prediction is worthwhile not only for the detection of orphan genes but also for understanding the mechanisms that regulate gene expression. Promoter prediction therefore remains one of the primary challenges subjects in bioinformatics in the post-genome era. Many methods are used for promoter prediction, such as the presence of the CpG islands, sequence motifs of transcription factor binding sites, and the statistical and chemo-physical properties in the vicinity of transcription start sites. Among these strategies, we have focused on a method which employs wavelet analysis and support vector machine for promoter prediction. The wavelet analysis is based on localized wave packets characterized by both a range of frequency and a location. In our scheme, information from promoter and non-promoter regions is converted to wavelet space as a positive and a negative set, respectively, and the 2 sets are subsequently used to train a support vector machine. Finally, the support vector machine is utilized for promoter prediction. In this study, we improved the coding method of our prediction strategy and analysed a new set of test data. © 2012 Published by Elsevier B.V.

Usui H.,Maebashi Institute of Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

When we use TeX to edit a document, it is sometimes necessary to place the figure of a preferred shape into a suitable position. In this presentation, we propose a method using KeTCindy for this purpose. KeTCindy is a plug-in to Cinderella that converts the procedure to generate geometric shapes into TeX readable code to generate the corresponding image on TeX final output. One merit of using KeTCindy is its interactive character. On the Cinderella screen, a user can control the shape of the figure as desired. When we place the resulting image at the exterior side of text part, simple conversion to TeX graphical image through KeTCindy is sufficient. However, when it is necessary to place it onto the text part, some extra elaboration is necessary to ensure that both the text part and the generated figure are finely balanced. The key idea is making the screen of Cinderella semi-transparent using software named feewhee. © Springer International Publishing Switzerland 2016.

Zhong N.,Beijing University of Technology | Zhong N.,Maebashi Institute of Technology | Chen J.,Beijing University of Technology
IEEE Transactions on Knowledge and Data Engineering | Year: 2012

The development of brain science has led to a vast increase of brain data. To meet requirements of a systematic methodology of Brain Informatics (BI), this paper proposes a new conceptual model of brain data, namely Data-Brain, which explicitly represents various relationships among multiple human brain data sources, with respect to all major aspects and capabilities of human information processing systems (HIPS). A multidimension framework and a BI methodology-based ontological modeling approach have been developed to implement a Data-Brain. The Data-Brain, Data-Brain-based BI provenances, and heterogeneous brain data can be used to construct a Data-Brain-based brain data center which provides a global framework to integrate data, information, and knowledge coming from the whole research process for systematic BI study. Such a Data-Brain modeling approach represents a radically new way for domain-driven conceptual modeling of brain data, which models a whole process of systematically investigating human information processing mechanisms. © 1989-2012 IEEE.

Zhong N.,Maebashi Institute of Technology | Zhong N.,Beijing University of Technology | Li Y.,Queensland University of Technology | Wu S.-T.,Asia University, Taiwan
IEEE Transactions on Knowledge and Data Engineering | Year: 2012

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance. © 2011 IEEE.

Tao X.,Queensland University of Technology | Li Y.,Queensland University of Technology | Zhong N.,Maebashi Institute of Technology
IEEE Transactions on Knowledge and Data Engineering | Year: 2011

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful. © 2011 IEEE.

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