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Genome informatics. International Conference on Genome Informatics. 2011;25(1):40-52.

Sign: large-scale gene network estimation environment for high performance computing

基于高性能计算的大规模基因网络估算环境 翻译改进

Yoshinori Tamada  1, Teppei Shimamura, Rui Yamaguchi, Seiya Imoto, Masao Nagasaki, Satoru Miyano

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作者单位

  • 1 Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan. tamada@ims.u-tokyo.ac.jp
  • PMID: 22230938

    摘要 Ai翻译

    Our research group is currently developing software for estimating large-scale gene networks from gene expression data. The software, called SiGN, is specifically designed for the Japanese flagship supercomputer "K computer" which is planned to achieve 10 petaflops in 2012, and other high performance computing environments including Human Genome Center (HGC) supercomputer system. SiGN is a collection of gene network estimation software with three different sub-programs: SiGN-BN, SiGN-SSM and SiGN-L1. In these three programs, five different models are available: static and dynamic nonparametric Bayesian networks, state space models, graphical Gaussian models, and vector autoregressive models. All these models require a huge amount of computational resources for estimating large-scale gene networks and therefore are designed to be able to exploit the speed of 10 petaflops. The software will be available freely for "K computer" and HGC supercomputer system users. The estimated networks can be viewed and analyzed by Cell Illustrator Online and SBiP (Systems Biology integrative Pipeline). The software project web site is available at http://sign.hgc.jp/ .

    Keywords:high performance computing

    Copyright © Genome informatics. International Conference on Genome Informatics. 中文内容为AI机器翻译,仅供参考!

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    Sign: large-scale gene network estimation environment for high performance computing