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    • 10. 发明申请
    • K-PARTITE GRAPH BASED FORMALISM FOR CHARACTERIZATION OF COMPLEX PHENOTYPES IN CLINICAL DATA ANALYSES AND DISEASE OUTCOME PROGNOSIS
    • 用于表征临床数据分析和疾病预后预测中复合基因的K分布图基于形式
    • US20140200824A1
    • 2014-07-17
    • US14212036
    • 2014-03-14
    • Petr Pancoska
    • Petr Pancoska
    • G06F19/24
    • G16B40/00G16B20/00
    • Systems and methods are disclosed that can analyze relationships between parameters in data matrices (e.g., collections of individual profiles). A graph topology can be defined on a data matrix with partitions as variables and vertices in all partitions and their potentials and edges as the co-occurrence of a pair of variable values in a profile. Individual graphs can be constructed from data and value co-occurrences for every profile, and a study data graph made as a union of all individual graphs. Heterogeneity Landmarks (HLs) can be determined from the study data graph, and graph-graph distances between individual graphs and all HLs. These distances can be used for prognoses based on similarity of a profile to one or more HLs.
    • 公开了可以分析数据矩阵中的参数之间的关系的系统和方法(例如,各个简档的集合)。 可以在数据矩阵上定义图形拓扑,其中分区作为所有分区中的变量和顶点,以及它们的电位和边缘,作为配置文件中一对变量值的同现。 可以从每个配置文件的数据和值共同构建单个图形,并将研究数据图形作为所有单独图形的并集。 可以从研究数据图中确定异质性标志(HL),以及各图和所有HL之间的图形图距离。 这些距离可以用于基于轮廓与一个或多个HL的相似性的预测。