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    • 23. 发明授权
    • Methods for testing biological network models
    • 生物网络模型测试方法
    • US06132969A
    • 2000-10-17
    • US99722
    • 1998-06-19
    • Roland StoughtonRichard M. Karp
    • Roland StoughtonRichard M. Karp
    • G01N33/50C12N15/09C12Q1/06C12Q1/68C12R1/865G01N33/15G06F19/12C12Q1/00G01N33/53G06F17/00
    • G06F19/12
    • The present invention provides methods and systems for testing and confirming how well a network model represents a biological pathway in a biological system. The network model comprises a network of logical operators relating input cellular constituents (e.g., mRNA or protein abundances) to output classes of cellular constituents, which are affected by the pathway in the biological system. The methods of this invention provide, first, for choosing complete and efficient experiments for testing the network model which compare relative changes in the biological system in response to perturbations of the network. The methods also provide for determining an overall goodness of fit of the network model to biological system by: predicting from the network model how output classes behave in response to the chosen experiments, finding measures of relative change of cellular constituents actually observed in the chosen experiments, finding goodnesses of fit of each observed cellular constituent to an output class with which the cellular constituent has the strongest correlation, and determining an overall goodness of fit of the network model from the individual goodnesses of fit of each observed cellular constituent. Additionally, these methods provide for testing the significance of the overall goodness of fit according to a nonparametric statistical test using an empirically determined distribution of possible goodnesses of fit. This invention also provides for computer systems for carrying out the computational steps of these methods.
    • 本发明提供用于测试和确认网络模型在生物系统中代表生物学途径的方式和系统。 网络模型包括将输入细胞成分(例如,mRNA或蛋白质丰度)与输出生物系统中途径影响的细胞成分类型相关联的逻辑运算器网络。 本发明的方法首先提供用于选择用于测试网络模型的完整和有效的实验,该网络模型响应于网络的扰动来比较生物系统中的相对变化。 该方法还提供了通过以下方式来确定网络模型对生物系统的整体拟合度:从网络模型预测输出类型如何响应于所选择的实验而行为,找到在所选实验中实际观察到的细胞成分的相对变化的度量 将每个观察到的细胞成分适合于细胞成分具有最强相关性的输出类别,并根据每个观察到的细胞成分的拟合优度确定网络模型的整体拟合度。 此外,这些方法提供了使用经验确定的可能的适合度的分布来根据非参数统计检验测试整体拟合度的重要性。 本发明还提供了用于执行这些方法的计算步骤的计算机系统。
    • 28. 发明授权
    • Methods of characterizing drug activities using consensus profiles
    • 使用共识的形式表征药物活动的方法
    • US06801859B1
    • 2004-10-05
    • US09220142
    • 1998-12-23
    • Stephen H. FriendRoland StoughtonYudong He
    • Stephen H. FriendRoland StoughtonYudong He
    • G01N3348
    • G06F19/24G06F19/20
    • The present invention provides methods for enhanced detection of biological response profiles. In particular, the methods of this invention allow for the detection of biological response patterns, such as gene expression patterns, in response to different drug treatments. The methods of the invention also allow the determination of a “consensus profile” which describes a particular class or type of biological response. In certain embodiments the consensus profile may describe the biological response of a particular group or class of drugs. In other embodiments, the consensus profile may describe an “ideal” biological response such as one associated with a desired therapeutic effect. The methods of the present invention also allow for the comparison of different biological responses. Thus, the methods of the invention may be used, e.g., to identify and/or study new drugs.
    • 本发明提供用于增强生物反应曲线检测的方法。 特别地,本发明的方法允许响应于不同的药物治疗来检测生物反应模式,如基因表达模式。 本发明的方法还允许确定描述特定类别或类型的生物反应的“共有概况”。 在某些实施方案中,共有概况可以描述特定组或一类药物的生物反应。 在其它实施方案中,共有概况可以描述“理想”生物反应,例如与期望的治疗效果相关联的生物反应。 本发明的方法还允许比较不同的生物反应。 因此,本发明的方法可以用于例如鉴定和/或研究新的药物。