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    SYSTEMS AND METHODS FOR ELECTRONICALLY MINING GENOMIC DATA
    3.
    发明申请
    SYSTEMS AND METHODS FOR ELECTRONICALLY MINING GENOMIC DATA 审中-公开
    电子采矿基因数据的系统和方法

    公开(公告)号：US20160132506A1

    公开(公告)日：2016-05-12

    申请号：US14938509

    申请日：2015-11-11

    申请人： THE REGENTS OF THE UNIVERSITY OF MICHIGAN

    发明人： Mark J. Kiel ,  Kojo Elenitoba-Johnson ,  Megan Lim

    IPC分类号： G06F17/30

    CPC分类号： G16B40/00 ,  G16B20/00

    摘要： A data analysis method and computer system electronically mines published articles from existing medical literature sources to discover associations that may exist between various diseases and various genes and/or gene mutations or other genetic changes. The method and system then organizes, categorizes and prioritizes the discovered associations in accordance with the strength of evidence supporting these associations. The resulting information can then be integrated into the processing of genome sequencing data to more quickly determine what genome sequencing data is of most relevance for clinical decision makings.

    摘要翻译： 数据分析方法和计算机系统以电子方式从现有医学文献来源发表文章，以发现各种疾病与各种基因和/或基因突变或其他遗传变化之间可能存在的关联。 然后，方法和系统根据支持这些协会的证据的实力组织，分类和确定发现的关联的优先级。 然后将所得信息整合到基因组测序数据的处理中，以更快速地确定什么基因组测序数据与临床决策制定最相关。

    Systems and methods for electronically mining genomic data
    5.
    发明授权
    Systems and methods for electronically mining genomic data 有权

    公开(公告)号：US10332617B2

    公开(公告)日：2019-06-25

    申请号：US14938509

    申请日：2015-11-11

    申请人： THE REGENTS OF THE UNIVERSITY OF MICHIGAN

    发明人： Mark J. Kiel ,  Kojo Elenitoba-Johnson ,  Megan Lim

    IPC分类号： G16B20/00 ,  G16B40/00

    摘要： A data analysis method and computer system electronically mines published articles from existing medical literature sources to discover associations that may exist between various diseases and various genes and/or gene mutations or other genetic changes. The method and system then organizes, categorizes and prioritizes the discovered associations in accordance with the strength of evidence supporting these associations. The resulting information can then be integrated into the processing of genome sequencing data to more quickly determine what genome sequencing data is of most relevance for clinical decision makings.

    COMPOSITIONS AND METHODS RELATING TO FUSION PROTEIN BIOMARKERS
    7.
    发明申请
    COMPOSITIONS AND METHODS RELATING TO FUSION PROTEIN BIOMARKERS 有权
    与融合蛋白生物标志相关的组合物和方法

    公开(公告)号：US20150031061A1

    公开(公告)日：2015-01-29

    申请号：US14317606

    申请日：2014-06-27

    申请人： THE REGENTS OF THE UNIVERSITY OF MICHIGAN

    发明人： Kojo Elenitoba-Johnson ,  Delphine Rolland ,  Venkatesha Basrur ,  Kevin P. Conlon ,  Megan S. Lim

    IPC分类号： C12Q1/48 ,  C12Q1/37

    CPC分类号： C12Q1/37 ,  G01N33/6848 ,  G01N2800/7028

    摘要： The present invention provides fusion proteins as biomarkers specific for chromosomal translocation-based conditions (e.g., cancer), related methods for detecting fusion protein biomarkers associated with chromosomal translocation-based conditions, related methods for quantifying amount of fusion protein expression, and related methods for diagnosing chromosomal translocation-based conditions through detection of such fusion protein biomarkers. Such fusion protein biomarkers and related methods additionally find use in research settings.

    摘要翻译： 本发明提供了融合蛋白作为基于染色体易位的条件（例如，癌症）特异性的生物标志物，用于检测与染色体易位基础条件相关的融合蛋白生物标志物的相关方法，用于定量融合蛋白表达量的相关方法以及相关方法 通过检测这种融合蛋白生物标志物来诊断基于染色体易位的病症。 此类融合蛋白生物标志物和相关方法还可用于研究设置。

    COMPUTER-IMPLEMENTED METHODS FOR AUTOMATED ANALYSIS AND PRIORITIZATION OF VARIANTS IN DATASETS
    9.
    发明申请
    COMPUTER-IMPLEMENTED METHODS FOR AUTOMATED ANALYSIS AND PRIORITIZATION OF VARIANTS IN DATASETS 审中-公开

    公开(公告)号：US20190042697A1

    公开(公告)日：2019-02-07

    申请号：US16155046

    申请日：2018-10-09

    申请人： THE REGENTS OF THE UNIVERSITY OF MICHIGAN

    发明人： Mark J. Kiel ,  Kojo Elenitoba-Johnson ,  Megan Lim

    IPC分类号： G06F19/24 ,  G06F19/28 ,  G16H50/20 ,  G06F19/18

    摘要： Computer-implemented methods for automating identification and prioritization of genomic variants are disclosed. Such methods employ a rule set to analyze information regarding statistical frequency of variants in a dataset and metrics indicating biological relatedness to generate a priority-score indicative of the relevance of each variant in the dataset. The methods perform both variant frequency normalization and universal pairwise variant comparisons across the datasets to automatically calculate the likelihood that each variant is significant to a disease or other biological phenomenon under study. Priority-scores are calculated for the variants based upon such pairwise comparisons, and the results are organized into a priority ranking, which may be used to categorize the results into data subsets for display to a user.