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    • 1. 发明授权
    • Methods for determining therapeutic index from gene expression profiles
    • 从基因表达谱确定治疗指数的方法
    • US08560243B2
    • 2013-10-15
    • US11879161
    • 2007-07-16
    • Matthew MartonRoland Stoughton
    • Matthew MartonRoland Stoughton
    • G06F17/00G06F17/11
    • C12Q1/6883C12Q1/025C12Q1/18C12Q2600/106C12Q2600/158G01N33/5005G01N33/5088G06F19/20G06F19/70
    • This invention provides methods for determining drug specificity, therapeutic index and effective doses for individual patients. According to the methods of the invention, graded levels of drug are applied to a biological sample or a patient. A plurality of cellular constituents are measured to determine the activity of the drug on a target pathway and at least one off-target pathway. A drug specificity is determined by comparing the target and off target activities of the drug. A therapeutic concentration (or dose) is defined as a concentration (or dose) of the drug that induces certain response in the target pathway. A toxic concentration (or dose) is defined as a concentration (or dose) of the drug that induces certain response in the off target pathway. Therapeutic index is the ratio of the toxic concentration over therapeutic concentration. Methods are also provided to determine an effective dose of a drug for a patient by measuring the activity of the drug on the particular patient.
    • 本发明提供用于确定个体患者的药物特异性,治疗指数和有效剂量的方法。 根据本发明的方法,将分级药物施用于生物样品或患者。 测量多个细胞成分以确定药物在靶途径和至少一个脱靶途径上的活性。 通过比较药物的靶标和脱靶活性来确定药物特异性。 治疗浓度(或剂量)被定义为在目标途径中诱导一定反应的药物的浓度(或剂量)。 毒性浓度(或剂量)被定义为在脱靶途径中诱导某些反应的药物的浓度(或剂量)。 治疗指标是毒性浓度与治疗浓度的比值。 还提供了通过测量特定患者上的药物的活性来确定患者药物的有效剂量的方法。
    • 7. 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。