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Methods and compositions utilizing evolutionary computation techniques and differential data sets

机译:利用进化计算技术和差分数据集的方法和组合

摘要

Systems biology models of biochemical systems have proved to be powerful conceptual tools for the analysis of biological data but have historically been arduous to formulate and test. Biological data is also unique in that it exists mostly in the differential display format, unsuitable for use in standard mathematical modeling efforts. The present invention removes the drudgery of model building and data analysis from the shoulders of the individual researcherswho are no longer able assimilate the overwhelming volume of bioinformatic data and synthesize this into a model of the underlying physiologyby providing an artificial intelligence (Al) substitute. In addition to the use of Al methods for building the invention describes the use of difference equations and linear algebra to recast the models into another mathematical domain, allowing the direct use of differential display data formats for model testing and eliminating the need for time-consuming numerical integration. The combined effect is to significantly accelerate the model building and testing process and provide more complete alternative models for physiological and other complex systems.
机译:生化系统的系统生物学模型已被证明是用于分析生物数据的强大概念工具,但历史上一直难以制定和测试。生物数据也是独特的,因为它主要以差异显示格式存在,不适合用于标准数学建模工作。本发明从不再能够吸收大量生物信息数据的个人研究者的肩上消除了模型构建和数据分析的烦恼,并且通过提供人工智能(Al)替代品将其合成为基础生理学模型。除了使用Al方法来构建本发明之外,本发明还描述了使用差分方程和线性代数将模型重铸到另一个数学域中,从而允许将差分显示数据格式直接用于模型测试并消除了耗时的需求数值积分。综合的效果是大大加快了模型的建立和测试过程,并为生理和其他复杂系统提供了更完整的替代模型。

著录项

  • 公开/公告号US2004133355A1

    专利类型

  • 公开/公告日2004-07-08

    原文格式PDF

  • 申请/专利权人 TARGET DISCOVERY INC.;

    申请/专利号US20030678041

  • 发明设计人 LUKE V. SCHNEIDER;

    申请日2003-10-01

  • 分类号G06F19/00;G01N33/48;G01N33/50;

  • 国家 US

  • 入库时间 2022-08-21 23:19:12

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