SYSTEMS AND METHODS FOR PERFORMING CORRELATION ANALYSIS ON CLINICAL OUTCOME AND CHARACTERISTICS OF BIOLOGICAL TISSUE
    3.
    发明申请
    SYSTEMS AND METHODS FOR PERFORMING CORRELATION ANALYSIS ON CLINICAL OUTCOME AND CHARACTERISTICS OF BIOLOGICAL TISSUE 有权
    对生物组织临床观察和特征进行相关分析的系统和方法

    公开(公告)号:US20130290006A1

    公开(公告)日:2013-10-31

    申请号:US13459999

    申请日:2012-04-30

    IPC分类号: G06Q50/22

    摘要: Embodiments disclosed herein include methods, systems, and devices for determining a positive or negative correlation between a clinical outcome and one or more features of biological tissue. An exemplary user interface enables a user to select a clinical outcome and one or more aspects of a displayed field-of-view of biological tissue. Exemplary embodiments may automatically perform correlation analysis between the selected clinical outcome and one or more features characteristic of the user-selected aspects of the field-of-view of biological tissue. For example, exemplary embodiments may automatically perform correlation analysis between the selected clinical outcome and one or more features characteristic of one or more biological units in the biological tissue.

    摘要翻译: 本文公开的实施方案包括用于确定临床结果与生物组织的一个或多个特征之间的正相关或负相关的方法,系统和装置。 示例性用户界面使得用户能够选择生物组织的显示视野的临床结果和一个或多个方面。 示例性实施例可以自动执行所选择的临床结果与生物组织的视野的用户选定方面特征的一个或多个特征之间的相关性分析。 例如,示例性实施例可以自动执行所选临床结果与生物组织中一个或多个生物单元特征的一个或多个特征之间的相关性分析。

    ANALYZING THE EXPRESSION OF BIOMARKERS IN CELLS WITH CLUSTERS
    4.
    发明申请
    ANALYZING THE EXPRESSION OF BIOMARKERS IN CELLS WITH CLUSTERS 审中-公开
    分析细胞中生物标志物的表达

    公开(公告)号:US20120271553A1

    公开(公告)日:2012-10-25

    申请号:US13252080

    申请日:2011-10-03

    IPC分类号: G06F19/10

    摘要: A data set of cell profile data is stored. The cell profile data includes multiplexed biometric image data describing the expression of a plurality of biomarkers. Cell profile data is generated from tissue samples drawn from a cohort of patients having an assessment related to the commonality. Multiple sets of clusters of similar cells are generated from the data set; the proportion of cells in each cluster is examined for an association with a diagnosis, a prognosis, or a response; and a predictive set of clusters is selected based on model performance. One predictive set of clusters is selected based on a comparison of the performance of at least one model of the plurality of sets of clusters. Display techniques that aid in understanding the characteristics of a cluster are disclosed.

    摘要翻译: 存储小区简档数据的数据集。 细胞谱图数据包括描述多个生物标志物的表达的多重生物统计图像数据。 从具有与共性相关的评估的患者队列中抽取的组织样品产生细胞谱图数据。 从数据集中生成多组类似的单元; 检查每个组中细胞的比例与诊断,预后或反应的关联; 并基于模型性能选择一组预测集群。 基于多组聚类中的至少一个模型的性能的比较来选择一组预测集群。 公开了有助于理解集群特征的显示技术。

    ANALYZING THE EXPRESSION OF BIOMARKERS IN CELLS WITH MOMENTS
    5.
    发明申请
    ANALYZING THE EXPRESSION OF BIOMARKERS IN CELLS WITH MOMENTS 审中-公开
    分析生物标志物在细胞中的表达

    公开(公告)号:US20120270752A1

    公开(公告)日:2012-10-25

    申请号:US13252069

    申请日:2011-10-03

    摘要: Cell profile data is stored. The cell profile data comprises multiplexed biometric images capturing the expression of a plurality of biomarkers by at least one tissue sample from a patient with respect to at least one field of view. Individual cells are delineated and segmented into compartments. At least one cell feature is calculated based on the cell's expression of each of a plurality of biomarkers. A first moment is calculated for each cell feature. The moments are examined for an association with a diagnosis, a prognosis of a response to treatment of a condition or disease. Apparatus for performing the foregoing steps are disclosed.

    摘要翻译: 存储单元格简档数据。 细胞分布数据包括相对于至少一个视场从患者至少一个组织样本捕获多个生物标志物的表达的多重生物统计图像。 单个细胞被描绘并分割成隔室。 基于多个生物标记中的每一个的细胞表达来计算至少一个细胞特征。 计算每个单元格特征的第一时刻。 检查时间与诊断的关联,对病症或疾病的治疗的反应的预后。 公开了用于执行上述步骤的装置。

    DETERMINATION OF SPATIAL PROXIMITY BETWEEN FEATURES OF INTEREST IN BIOLOGICAL TISSUE
    7.
    发明申请
    DETERMINATION OF SPATIAL PROXIMITY BETWEEN FEATURES OF INTEREST IN BIOLOGICAL TISSUE 有权
    确定生物组织利益特征之间的空间接近度

    公开(公告)号:US20140003702A1

    公开(公告)日:2014-01-02

    申请号:US13539187

    申请日:2012-06-29

    IPC分类号: G06K9/46

    摘要: Exemplary embodiments enable determination of spatial proximity between two or more features in biological tissue. An exemplary method includes identifying a morphological feature in an image of the biological tissue based on expression levels of a first biomarker indicative of the morphological feature, and receiving a result of a segmentation analysis performed on the image of the biological tissue identifying a set of morphological units in the image external to the morphological feature. An exemplary method includes determining an expression level of a second biomarker corresponding to each unit in the set of morphological units in the image of the biological tissue, and determining a spatial distance between the morphological feature and each unit in the set of morphological units. An exemplary method further includes automatically determining a relationship between expression levels of the second biomarker and corresponding spatial distance from the morphological feature of the set of morphological units.

    摘要翻译: 示例性实施例使得能够确定生物组织中两个或更多个特征之间的空间接近度。 一种示例性方法包括基于指示形态学特征的第一生物标志物的表达水平鉴定生物组织的图像中的形态学特征,以及接收对生物组织的图像执行的识别一组形态学的分割结果的结果 图像中的单位在形态特征之外。 一种示例性方法包括确定与生物组织的图像中的形态单元组中的每个单元相对应的第二生物标志物的表达水平,以及确定该形态单位组中的形态特征与每个单位之间的空间距离。 一种示例性方法还包括自动确定第二生物标志物的表达水平与相应的空间距离之间的关系,与该组形态学单元的形态学特征。

    Determination of spatial proximity between features of interest in biological tissue
    9.
    发明授权
    Determination of spatial proximity between features of interest in biological tissue 有权
    确定生物组织中感兴趣的特征之间的空间接近度

    公开(公告)号:US08873827B2

    公开(公告)日:2014-10-28

    申请号:US13539187

    申请日:2012-06-29

    IPC分类号: G06K9/00

    摘要: Exemplary embodiments enable determination of spatial proximity between two or more features in biological tissue. An exemplary method includes identifying a morphological feature in an image of the biological tissue based on expression levels of a first biomarker indicative of the morphological feature, and receiving a result of a segmentation analysis performed on the image of the biological tissue identifying a set of morphological units in the image external to the morphological feature. An exemplary method includes determining an expression level of a second biomarker corresponding to each unit in the set of morphological units in the image of the biological tissue, and determining a spatial distance between the morphological feature and each unit in the set of morphological units. An exemplary method further includes automatically determining a relationship between expression levels of the second biomarker and corresponding spatial distance from the morphological feature of the set of morphological units.

    摘要翻译: 示例性实施例使得能够确定生物组织中两个或更多个特征之间的空间接近度。 一种示例性方法包括基于指示形态学特征的第一生物标志物的表达水平鉴定生物组织的图像中的形态学特征,以及接收对生物组织的图像执行的识别一组形态学的分割结果的结果 图像中的单位在形态特征之外。 一种示例性方法包括确定与生物组织的图像中的形态单元组中的每个单元相对应的第二生物标志物的表达水平,以及确定该形态单位组中的形态特征与每个单位之间的空间距离。 一种示例性方法还包括自动确定第二生物标志物的表达水平与相应的空间距离之间的关系,与该组形态学单元的形态学特征。

    SYSTEM AND METHOD FOR TRANSACTIONAL RISK AND RETURN ANALYSIS
    10.
    发明申请
    SYSTEM AND METHOD FOR TRANSACTIONAL RISK AND RETURN ANALYSIS 审中-公开
    用于交易风险和回报分析的系统和方法

    公开(公告)号:US20130226830A1

    公开(公告)日:2013-08-29

    申请号:US13407623

    申请日:2012-02-28

    IPC分类号: G06Q40/06

    CPC分类号: G06Q40/00

    摘要: Transactional risk and return analysis systems provided herein include a transaction database and a market database. The transaction database includes data regarding transactions with associated attributes and the market database includes market data. A portfolio model uses such data to estimate a risk prediction for each transaction. A risk prediction model is generated based on the portfolio model and estimates a risk prediction for a prospective transaction, and a case cash flow analyzer produces a risk-breakeven spread. A transaction evaluator uses the risk prediction model and the risk-breakeven spread to calculated transaction risk and return data for a prospective transaction.

    摘要翻译: 本文提供的交易风险和回报分析系统包括交易数据库和市场数据库。 交易数据库包括关于具有关联属性的交易的数据,并且市场数据库包括市场数据。 投资组合模型使用这些数据来估计每笔交易的风险预测。 基于投资组合模型生成风险预测模型,并估计潜在交易的风险预测,案例现金流量分析仪产生风险承受偏差。 交易评估者使用风险预测模型和风险 - 盈亏平衡差来计算交易风险和预期交易的回报数据。