Mikio Yoshida

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Comprehensive analysis of protein-protein interactions is a challenging endeavor of functional proteomics and has been best explored in the budding yeast. The yeast protein interactome analysis was achieved first by using the yeast two-hybrid system in a proteome-wide scale and next by large-scale mass spectrometric analysis of affinity-purified protein(More)
MOTIVATION Since their initial development, integration and construction of databases for molecular-level data have progressed. Though biological molecules are related to each other and form a complex system, the information is stored in the vast archives of the literature or in diverse databases. There is no unified naming convention for biological object,(More)
BACKGROUND An ideal format to describe transcriptome would be its composition measured on the scale of absolute numbers of individual mRNAs per cell. It would help not only to precisely grasp the structure of the transcriptome but also to accelerate data exchange and integration. RESULTS We conceived an idea of competitive PCR between genomic DNA and(More)
BACKGROUND We have developed genetic methods in zebrafish by using the Tol2 transposable element; namely, transgenesis, gene trapping, enhancer trapping and the Gal4FF-UAS system. Gene trap constructs contain a splice acceptor and the GFP or Gal4FF (a modified version of the yeast Gal4 transcription activator) gene, and enhancer trap constructs contain the(More)
Since their advent, interaction with computers has been a very fascinating field of research. Though, we have come a long way from turning knobs and punching cards to using keyboards and pointing devices, natural language interaction has not seen widespread use as a general means of interaction. The thesis of this paper is that some application fields,(More)
Recently, a network-based computational-learning system, ATN (Algorithmically Transitive Network), was proposed by the authors [1]. The ATN represents algorithm by a data-flow network (topology) and revises the algorithm by the aid of a learning method for the artificial neural network. In this abstract, we extend the ATN to KTN (Knowledge Transitive(More)