Methods for analyzing high dimensional data for classifying, diagnosing, prognosticating, and/or predicting diseases and other biological states
    1.
    发明授权
    Methods for analyzing high dimensional data for classifying, diagnosing, prognosticating, and/or predicting diseases and other biological states 有权
    分析高维数据以分类,诊断,预测和/或预测疾病和其他生物状态的方法

    公开(公告)号:US07783431B2

    公开(公告)日:2010-08-24

    申请号:US11928901

    申请日:2007-10-30

    摘要: A method of diagnosing, predicting, or prognosticating about a disease that includes obtaining experimental data, wherein the experimental data is high dimensional data, filtering the data, reducing the dimensionality of the data through use of one or more methods, training a supervised pattern recognition method, ranking individual data points from the data, wherein the ranking is dependent on the outcome of the supervised pattern recognition method, choosing multiple data points from the data, wherein the choice is based on the relative ranking of the individual data points, and using the multiple data points to determine if an unknown set of experimental data indicates a diseased condition, a predilection for a diseased condition, or a prognosis about a diseased condition.

    摘要翻译: 一种诊断,预测或预测疾病的方法,包括获得实验数据,其中实验数据是高维数据,过滤数据,通过使用一种或多种方法降低数据的维度,训练监督模式识别 方法,从数据中排列各个数据点,其中排序取决于监督模式识别方法的结果,从数据中选择多个数据点,其中所述选择基于各个数据点的相对排名,并且使用 多个数据点以确定未知的一组实验数据是否表示疾病状况,患病条件的偏好或关于患病状况的预后。

    Methods for Analyzing High Dimension Data for Classifying, Diagnosing, Prognosticating, and/or Predicting Diseases and Other Biological States
    3.
    发明申请
    Methods for Analyzing High Dimension Data for Classifying, Diagnosing, Prognosticating, and/or Predicting Diseases and Other Biological States 有权
    分析,诊断,预测和/或预测疾病和其他生物状态的高维数据的方法

    公开(公告)号:US20090035766A1

    公开(公告)日:2009-02-05

    申请号:US11928901

    申请日:2007-10-30

    IPC分类号: C12Q1/68 A61B5/00

    摘要: A method of diagnosing, predicting, or prognosticating about a disease that includes obtaining experimental data, wherein the experimental data is high dimensional data, filtering the data, reducing the dimensionality of the data through use of one or more methods, training a supervised pattern recognition method, ranking individual data points from the data, wherein the ranking is dependent on the outcome of the supervised pattern recognition method, choosing multiple data points from the data, wherein the choice is based on the relative ranking of the individual data points, and using the multiple data points to determine if an unknown set of experimental data indicates a diseased condition, a predilection for a diseased condition, or a prognosis about a diseased condition.

    摘要翻译: 一种诊断,预测或预测疾病的方法,包括获得实验数据,其中实验数据是高维数据,过滤数据,通过使用一种或多种方法降低数据的维度,训练监督模式识别 方法,从数据中排列各个数据点,其中排序取决于监督模式识别方法的结果,从数据中选择多个数据点,其中所述选择基于各个数据点的相对排名,并且使用 多个数据点以确定未知的一组实验数据是否表示疾病状况,患病条件的偏好或关于患病状况的预后。

    Sets of probes and primers for the diagnosis of select cancers
    4.
    发明授权
    Sets of probes and primers for the diagnosis of select cancers 失效
    用于诊断选择性癌症的探针和引物组

    公开(公告)号:US08263759B2

    公开(公告)日:2012-09-11

    申请号:US11981502

    申请日:2007-10-30

    IPC分类号: C07H21/02 C07H21/04 C12Q1/68

    CPC分类号: G06F19/24 G06F19/20 Y02A90/26

    摘要: A method of diagnosing a disease that includes obtaining experimental data on gene selections. The gene selection functions to characterize a cancer when the expression of that gene selection is compared to the identical selection from a noncancerous cell or a different type of cancer cell. The invention also includes a method of targeting at least one product of a gene that includes administration of a therapeutic agent. The invention also includes the use of a gene selection for diagnosing a cancer.

    摘要翻译: 一种诊断疾病的方法,包括获得关于基因选择的实验数据。 当将该基因选择的表达与来自非癌细胞或不同类型的癌细胞的相同选择进行比较时,基因选择用于表征癌症。 本发明还包括靶向至少一种包括施用治疗剂的基因产物的方法。 本发明还包括使用基因选择来诊断癌症。

    Method for protein structure alignment
    6.
    发明授权
    Method for protein structure alignment 失效
    蛋白质结构对齐方法

    公开(公告)号:US06859736B2

    公开(公告)日:2005-02-22

    申请号:US09825441

    申请日:2001-04-02

    CPC分类号: G06F19/16 G06F19/22

    摘要: This invention provides a method for protein structure alignment. More particularly, the present invention provides a method for identification, classification and prediction of protein structures. The present invention involves two key ingredients. First, an energy or cost function formulation of the problem simultaneously in terms of binary (Potts) assignment variables and real-valued atomic coordinates. Second, a minimization of the energy or cost function by an iterative method, where in each iteration (1) a mean field method is employed for the assignment variables and (2) exact rotation and/or translation of atomic coordinates is performed, weighted with the corresponding assignment variables.

    摘要翻译: 本发明提供蛋白质结构对准的方法。 更具体地,本发明提供了蛋白质结构的鉴定,分类和预测的方法。 本发明涉及两个关键成分。 首先,在二值(Potts)赋值变量和实值原子坐标方面同时考虑问题的能量或成本函数公式。 第二,通过迭代方法最小化能量或成本函数,其中在每次迭代(1)中使用平均场方法用于分配变量,并且(2)精确旋转和/或原子坐标的平移被执行,用 相应的赋值变量。