Coupled two-way clustering analysis of data
    2.
    发明授权
    Coupled two-way clustering analysis of data 失效
    耦合双向数据聚类分析

    公开(公告)号:US06965831B2

    公开(公告)日:2005-11-15

    申请号:US10220702

    申请日:2001-03-09

    摘要: A novel coupled two-way clustering approach to gene microarray data analysis, for identifying subsets of the genes and samples, such that when one of these items is used to cluster the other, stable and significant partitions emerge. The method of the present invention preferably uses iterative clustering in order to execute this search in an efficient way. This approach is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method of the present invention was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on these subsets, partitions and correlations were found that were masked and hidden when the full data set was used in the analysis.

    摘要翻译: 一种新颖的双向聚类方法,用于基因芯片数据分析,用于识别基因和样本的子集,使得当这些项目中的一个被用于聚集另一个项目时,出现稳定和显着的分区。 本发明的方法优选地使用迭代聚类,以便以有效的方式执行该搜索。 这种方法特别适用于基因微阵列数据,其中各种生物机制对基因表达水平的贡献在大量实验数据中纠缠。 将本发明的方法应用于结肠癌和白血病的两个基因微阵列数据集。 通过识别数据的相关子集并集中在这些子集上,当分析中使用完整的数据集时,发现分区和相关性被掩蔽和隐藏。

    Coupled two-way clustering analysis of data
    3.
    发明授权
    Coupled two-way clustering analysis of data 失效
    耦合双向数据聚类分析

    公开(公告)号:US07599933B2

    公开(公告)日:2009-10-06

    申请号:US11154542

    申请日:2005-06-17

    IPC分类号: G06F17/30 G06F19/00

    摘要: A novel coupled two-way clustering approach to gene microarray data analysis, for identifying subsets of the genes and samples, such that when one of these items is used to cluster the other, stable and significant partitions emerge. The method of the present invention preferably uses iterative clustering in order to execute this search in an efficient way. This approach is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method of the present invention was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on these subsets, partitions and correlations were found that were masked and hidden when the full data set was used in the analysis.

    摘要翻译: 一种新颖的双向聚类方法,用于基因芯片数据分析,用于识别基因和样本的子集,使得当这些项目中的一个被用于聚集另一个项目时,出现稳定和显着的分区。 本发明的方法优选地使用迭代聚类,以便以有效的方式执行该搜索。 这种方法特别适用于基因微阵列数据,其中各种生物机制对基因表达水平的贡献在大量实验数据中纠缠。 将本发明的方法应用于结肠癌和白血病的两个基因微阵列数据集。 通过识别数据的相关子集并集中在这些子集上,当分析中使用完整的数据集时,发现分区和相关性被掩蔽和隐藏。

    Method and apparatus for clustering data
    4.
    发明授权
    Method and apparatus for clustering data 失效
    用于聚类数据的方法和装置

    公开(公告)号:US6021383A

    公开(公告)日:2000-02-01

    申请号:US725960

    申请日:1996-10-07

    IPC分类号: G06F17/30 G06K9/62 G06F17/00

    CPC分类号: G06K9/622 G06F17/30705

    摘要: A method and apparatus for partitioning a data set for clustering, based on the physical properties of an inhomogeneous ferromagnet. No assumption is made regarding the underlying distribution of the data. A Potts spin is assigned to each data point and an interaction between neighboring points is introduced, whose strength is a decreasing function of the distance between the neighbors. This magnetic system exhibits three phases. At very low temperatures it is completely ordered; i.e. all spins are aligned. At very high temperatures the system does not exhibit any ordering and in an intermediate regime clusters of relatively strongly coupled spins become ordered, whereas different clusters remain uncorrelated. This intermediate phase is identified by a jump in the order parameters. The spin--spin correlation function is used to partition the spins and the corresponding data points into clusters.

    摘要翻译: 基于不均匀铁磁体的物理特性来分割用于聚类的数据集的方法和装置。 对数据的底层分布没有假设。 将Potts旋转分配给每个数据点,并引入相邻点之间的交互,其强度是邻居之间距离的递减函数。 该磁系统呈现三相。 在非常低的温度下,它是完全有序的; 即所有自旋都对齐。 在非常高的温度下,系统不显示任何排序,并且在中间状态下,相对强耦合的旋转簇被排序,而不同的簇保持不相关。 该中间阶段通过顺序参数的跳转来识别。 自旋相关函数用于将自旋和相应的数据点分割成簇。

    Antigen array and diagnostic uses thereof
    7.
    发明申请
    Antigen array and diagnostic uses thereof 审中-公开
    抗原阵列及其诊断用途

    公开(公告)号:US20050260770A1

    公开(公告)日:2005-11-24

    申请号:US11094142

    申请日:2005-03-31

    IPC分类号: G01N33/543 G01N33/564

    CPC分类号: G01N33/564

    摘要: A method of diagnosing an immune disease, or a predisposition thereto, in a subject is disclosed. The method comprises determining a capacity of immunoglobulins of the subject to specifically bind each antigen probe of an antigen probe set, wherein the antigen probe set comprises a plurality of antigen probes selected from the group consisting of at least a portion of a cell/tissue structure molecule, at least a portion of a heat shock protein, at least a portion of an immune system molecule, at least a portion of a homopolymeric polypeptide, at least a portion of a hormone, at least a portion of a metabolic enzyme, at least a portion of a microbial antigen, at least a portion of a molluscan antigen, at least a portion of a nucleic acid, at least a portion of a plant antigen, at least a portion of plasma molecule, and at least a portion of a tissue antigen, wherein the capacity is indicative of the immune disease or the predisposition thereto, thereby diagnosing the immune disease, or the predisposition thereto, in the subject.

    摘要翻译: 公开了一种在受试者中诊断免疫疾病或其倾向的方法。 该方法包括确定受试者的免疫球蛋白的能力以特异性结合抗原探针组的每个抗原探针,其中所述抗原探针组包含多个选自下组的抗原探针:至少一部分细胞/组织结构 分子,至少一部分热休克蛋白,至少一部分免疫系统分子,至少一部分均聚多肽,至少一部分激素,代谢酶的至少一部分,至少 一部分微生物抗原,至少一部分软体动物素抗原,至少一部分核酸,至少一部分植物抗原,至少一部分血浆分子和至少一部分组织 抗原,其中所述能力指示免疫疾病或其易感性,从而诊断受试者的免疫疾病或其易感性。

    Coupled two-way clustering analysis of data
.

    公开(公告)号:US20050240563A1

    公开(公告)日:2005-10-27

    申请号:US11154542

    申请日:2005-06-17

    摘要: A novel coupled two-way clustering approach to gene microarray data analysis, for identifying subsets of the genes and samples, such that when one of these items is used to cluster the other, stable and significant partitions emerge. The method of the present invention preferably uses iterative clustering in order to execute this search in an efficient way. This approach is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method of the present invention was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on these subsets, partitions and correlations were found that were masked and hidden when the full data set was used in the analysis.