公开(公告)号：US07672789B2

公开(公告)日：2010-03-02

申请号：US11413183

申请日：2006-04-28

申请人： Tse-Wei Wang ,  Ning Xue ,  John D Birdwell ,  Mark Rader ,  John Flaherty

发明人： Tse-Wei Wang ,  Ning Xue ,  John D Birdwell ,  Mark Rader ,  John Flaherty

IPC分类号： G06F7/00

CPC分类号： G06F19/18 ,  G06F19/22 ,  G06F19/24 ,  Y10S707/99943

摘要： Least Square Deconvolution (LSD) uses quantitative allele peak area derived from a sample containing the DNA of more than one contributor to resolve the best-fit genotype profile of each contributor. The resolution is based on finding the least square fit of the mass ratio coefficients at each locus to come closest to the quantitative allele peak data. Consistent top-ranked mass ratio combinations from each locus can be pooled to form at least one composite DNA profile at a subset of the available loci. The top-ranked DNA profiles can be used to check against the profile of a suspect or be used to search for a matching profile in a DNA database.

摘要翻译： 最小二乘解卷积（LSD）使用从含有多个贡献者的DNA的样品衍生的定量等位基因峰面积来解决每个贡献者的最佳拟合基因型。 该分辨率基于找到每个轨迹处的质量比系数的最小二乘拟合最接近定量等位基因峰数据。 可以汇集来自每个基因座的一致的顶级质量比组合，以在可用基因座的子集处形成至少一个复合DNA谱。 排名最高的DNA谱可用于检查疑似病例的轮廓，或用于在DNA数据库中搜索匹配的分布。

公开(公告)号：US20110093208A1

公开(公告)日：2011-04-21

申请号：US12947063

申请日：2010-11-16

申请人： Tse-Wei Wang ,  Ning Xue ,  John D. Birdwell ,  Mark Rader ,  John Flaherty

发明人： Tse-Wei Wang ,  Ning Xue ,  John D. Birdwell ,  Mark Rader ,  John Flaherty

IPC分类号： G06F19/00

CPC分类号： G06F19/18 ,  G06F19/22 ,  G06F19/24 ,  Y10S707/99943

摘要： Least Square Deconvolution (LSD) uses quantitative allele peak data, for example, allele peak area, allele peak height and optical density, derived/obtained from a sample containing the DNA of more than one contributor to resolve the best-fit genotype profile of each contributor. The resolution is based on finding the least square fit of the mass ratio coefficients at each locus to come closest to the quantitative allele peak data. Consistent top-ranked mass ratio combinations from each locus can be pooled to form at least one composite DNA profile at a subset of the available loci. The top-ranked DNA profiles can be used to check against the profile of a suspect or be used to search for a matching profile in a DNA database.

摘要翻译： 最小二乘解卷积（LSD）使用定量等位基因峰数据，例如从包含多于一个贡献者的DNA的样品得到/获得的等位基因峰面积，等位基因峰高度和光密度，以解决每一个的最佳拟合基因型谱 贡献者。 该分辨率基于找到每个轨迹处的质量比系数的最小二乘拟合最接近定量等位基因峰数据。 可以汇集来自每个基因座的一致的顶级质量比组合，以在可用基因座的子集处形成至少一个复合DNA谱。 排名最高的DNA谱可用于检查疑似病例的轮廓，或用于在DNA数据库中搜索匹配的分布。

公开(公告)号：US20100153019A1

公开(公告)日：2010-06-17

申请号：US12631032

申请日：2009-12-04

申请人： Tse-Wei Wang ,  Ning Xue ,  John D. Birdwell ,  Mark Rader ,  John Flaherty

发明人： Tse-Wei Wang ,  Ning Xue ,  John D. Birdwell ,  Mark Rader ,  John Flaherty

IPC分类号： G06F19/00

CPC分类号： G06F19/18 ,  G06F19/22 ,  G06F19/24 ,  Y10S707/99943

摘要： Least Square Deconvolution (LSD) uses quantitative allele peak data derived obtained from a sample containing the DNA of more than one contributor to resolve the best-fit genotype profile of each contributor. The resolution is based on finding the least square fit of the mass ratio coefficients at each locus to come closest to the quantitative allele peak data. Consistent top-ranked mass ratio combinations from each locus can be pooled to form at least one composite DNA profile at a subset of the available loci. The top-ranked DNA profiles can be used to check against the profile of a suspect or be used to search for a matching profile in a DNA database.

摘要翻译： 最小二乘解卷积（LSD）使用从含有多个贡献者的DNA的样品获得的定量等位基因峰数据来解决每个贡献者的最佳拟合基因型谱。 该分辨率基于找到每个轨迹处的质量比系数的最小二乘拟合最接近定量等位基因峰数据。 可以汇集来自每个基因座的一致的顶级质量比组合，以在可用基因座的子集处形成至少一个复合DNA谱。 排名最高的DNA谱可用于检查疑似病例的轮廓，或用于在DNA数据库中搜索匹配的分布。

公开(公告)号：US07162372B2

公开(公告)日：2007-01-09

申请号：US10265908

申请日：2002-10-08

申请人： Tse-Wei Wang ,  Ning Xue ,  John D. Birdwell ,  Mark Rader ,  John Flaherty

发明人： Tse-Wei Wang ,  Ning Xue ,  John D. Birdwell ,  Mark Rader ,  John Flaherty

IPC分类号： G01N33/48 ,  G06F7/00 ,  C12Q1/68

CPC分类号： G06F19/18 ,  G06F19/22 ,  G06F19/24 ,  Y10S707/99943

摘要： Least Square Deconvolution (LSD) uses quantitative allele peak data derived obtained from a sample containing the DNA of more than one contributor to resolve the best-fit genotype profile of each contributor. The resolution is based on finding the least square fit of the mass ratio coefficients at each locus to come closest to the quantitative allele peak data. Consistent top-ranked mass ratio combinations from each locus can be pooled to form at least one composite DNA profile at a subset of the available loci. The top-ranked DNA profiles can be used to check against the profile of a suspect or be used to search for a matching profile in a DNA database.

摘要翻译： 最小二乘解卷积（LSD）使用从含有多个贡献者的DNA的样品获得的定量等位基因峰数据来解决每个贡献者的最佳拟合基因型谱。 该分辨率基于找到每个轨迹处的质量比系数的最小二乘拟合最接近定量等位基因峰数据。 可以汇集来自每个基因座的一致的顶级质量比组合，以在可用基因座的子集处形成至少一个复合DNA谱。 排名最高的DNA谱可用于检查疑似病例的轮廓，或用于在DNA数据库中搜索匹配的分布。

公开(公告)号：US20060190194A1

公开(公告)日：2006-08-24

申请号：US11413183

申请日：2006-04-28

申请人： Tse-Wei Wang ,  Ning Xue ,  John Birdwell ,  Mark Rader ,  John Flaherty

发明人： Tse-Wei Wang ,  Ning Xue ,  John Birdwell ,  Mark Rader ,  John Flaherty

IPC分类号： G06F19/00 ,  H04L9/00

CPC分类号： G06F19/18 ,  G06F19/22 ,  G06F19/24 ,  Y10S707/99943

摘要： Least Square Deconvolution (LSD) uses quantitative allele peak area derived from a sample containing the DNA of more than one contributor to resolve the best-fit genotype profile of each contributor. The resolution is based on finding the least square fit of the mass ratio coefficients at each locus to come closest to the quantitative allele peak data. Consistent top-ranked mass ratio combinations from each locus can be pooled to form at least one composite DNA profile at a subset of the available loci. The top-ranked DNA profiles can be used to check against the profile of a suspect or be used to search for a matching profile in a DNA database.

摘要翻译： 最小二乘解卷积（LSD）使用从含有多个贡献者的DNA的样品衍生的定量等位基因峰面积来解决每个贡献者的最佳拟合基因型。 该分辨率基于找到每个轨迹处的质量比系数的最小二乘拟合最接近定量等位基因峰数据。 可以汇集来自每个基因座的一致的顶级质量比组合，以在可用基因座的子集处形成至少一个复合DNA谱。 排名最高的DNA谱可用于检查疑似病例的轮廓，或用于在DNA数据库中搜索匹配的分布。

公开(公告)号：US07860661B2

公开(公告)日：2010-12-28

申请号：US12631032

申请日：2009-12-04

申请人： Tse-Wei Wang ,  Xue Ning ,  John Douglas Birdwell ,  Mark Rader ,  John Flaherty

发明人： Tse-Wei Wang ,  Xue Ning ,  John Douglas Birdwell ,  Mark Rader ,  John Flaherty

IPC分类号： G06F7/00

CPC分类号： G06F19/18 ,  G06F19/22 ,  G06F19/24 ,  Y10S707/99943

摘要： Least Square Deconvolution (LSD) uses quantitative allele peak data derived obtained from a sample containing the DNA of more than one contributor to resolve the best-fit genotype profile of each contributor. The resolution is based on finding the least square fit of the mass ratio coefficients at each locus to come closest to the quantitative allele peak data. Consistent top-ranked mass ratio combinations from each locus can be pooled to form at least one composite DNA profile at a subset of the available loci. The top-ranked DNA profiles can be used to check against the profile of a suspect or be used to search for a matching profile in a DNA database.

摘要翻译： 最小二乘解卷积（LSD）使用从含有多个贡献者的DNA的样品获得的定量等位基因峰数据来解决每个贡献者的最佳拟合基因型谱。 该分辨率基于找到每个轨迹处的质量比系数的最小二乘拟合最接近定量等位基因峰数据。 可以汇集来自每个基因座的一致的顶级质量比组合，以在可用基因座的子集处形成至少一个复合DNA谱。 排名最高的DNA谱可用于检查疑似病例的轮廓，或用于在DNA数据库中搜索匹配的分布。

公开(公告)号：US20090055361A1

公开(公告)日：2009-02-26

申请号：US12199010

申请日：2008-08-27

申请人： John D. Birdwell ,  Tse-Wei Wang ,  Roger D. Horn ,  Puneet Yadav ,  David J. Icove

发明人： John D. Birdwell ,  Tse-Wei Wang ,  Roger D. Horn ,  Puneet Yadav ,  David J. Icove

IPC分类号： G06F17/30

CPC分类号： G06F17/30327 ,  G06F17/30333 ,  G06F17/30598 ,  G06F19/24 ,  G06F19/28 ,  Y10S707/99932 ,  Y10S707/99933 ,  Y10S707/99935 ,  Y10S707/99942 ,  Y10S707/99945

摘要： A tree-structured index to multidimensional data is created using naturally occurring patterns and clusters within the data which permit efficient search and retrieval strategies in a database of DNA profiles. A search engine utilizes hierarchical decomposition of the database by identifying clusters of similar DNA profiles and maps to parallel computer architecture, allowing scale up past previously feasible limits. Key benefits of the new method are logarithmic scale up and parallelization. These benefits are achieved by identification and utilization of naturally occurring patterns and clusters within stored data. The patterns and clusters enable the stored data to be partitioned into subsets of roughly equal size. The method can be applied recursively, resulting in a database tree that is balanced, meaning that all paths or branches through the tree have roughly the same length. The method achieves high performance by exploiting the natural structure of the data in a manner that maintains balanced trees. Implementation of the method maps naturally to parallel computer architectures, allowing scale up to very large databases.

摘要翻译： 使用数据中的自然发生的模式和集群创建树形结构的多维数据索引，这些数据允许DNA简档数据库中的高效搜索和检索策略。 搜索引擎利用数据库的分层分解，通过识别类似DNA分布的集群并将其映射到并行计算机体系结构，从而超越以前可行的限制。 新方法的主要优点是对数放大和并行化。 这些优点通过识别和利用存储数据中的自然发生的模式和集群来实现。 模式和集群使存储的数据能够被划分成大致相等大小的子集。 该方法可以递归地应用，导致数据库树是平衡的，意味着通过树的所有路径或分支具有大致相同的长度。 该方法通过以保持平衡树的方式利用数据的自然结构来实现高性能。 该方法的实现自然映射到并行计算机体系结构，允许扩展到非常大的数据库。

公开(公告)号：US07454411B2

公开(公告)日：2008-11-18

申请号：US10767776

申请日：2004-01-30

申请人： John D. Birdwell ,  Tse-Wei Wang ,  Roger D. Horn ,  Puneet Yadav ,  David J. Icove

发明人： John D. Birdwell ,  Tse-Wei Wang ,  Roger D. Horn ,  Puneet Yadav ,  David J. Icove

IPC分类号： G06F17/30 ,  G06F7/00 ,  G06F17/00

CPC分类号： G06F17/30327 ,  G06F17/30333 ,  G06F17/30598 ,  G06F19/24 ,  G06F19/28 ,  Y10S707/99932 ,  Y10S707/99933 ,  Y10S707/99935 ,  Y10S707/99942 ,  Y10S707/99945

摘要： A tree-structured index to multidimensional data is created using naturally occurring patterns and clusters within the data which permit efficient search and retrieval strategies in a database of DNA profiles. A search engine utilizes hierarchical decomposition of the database by identifying clusters of similar DNA profiles and maps to parallel computer architecture, allowing scale up past previously feasible limits. Key benefits of the new method are logarithmic scale up and parallelization. These benefits are achieved by identification and utilization of naturally occurring patterns and clusters within stored data. The patterns and clusters enable the stored data to be partitioned into subsets of roughly equal size. The method can be applied recursively, resulting in a database tree that is balanced, meaning that all paths or branches through the tree have roughly the same length. The method achieves high performance by exploiting the natural structure of the data in a manner that maintains balanced trees. Implementation of the method maps naturally to parallel computer architectures, allowing scale up to very large databases.

摘要翻译： 使用数据中的自然发生的模式和集群创建树形结构的多维数据索引，这些数据允许DNA简档数据库中的高效搜索和检索策略。 搜索引擎利用数据库的分层分解，通过识别类似DNA分布的集群并将其映射到并行计算机体系结构，从而超越以前可行的限制。 新方法的主要优点是对数放大和并行化。 这些优点通过识别和利用存储数据中的自然发生的模式和集群来实现。 模式和集群使存储的数据能够被分割成大致相等大小的子集。 该方法可以递归地应用，导致数据库树是平衡的，意味着通过树的所有路径或分支具有大致相同的长度。 该方法通过以保持平衡树的方式利用数据的自然结构来实现高性能。 该方法的实现自然映射到并行计算机体系结构，允许扩展到非常大的数据库。

公开(公告)号：US20080172402A1

公开(公告)日：2008-07-17

申请号：US11836836

申请日：2007-08-10

申请人： John D. Birdwell ,  Tse-Wei Wang ,  Roger D. Horn ,  Puneet Yadav ,  David J. Icove

发明人： John D. Birdwell ,  Tse-Wei Wang ,  Roger D. Horn ,  Puneet Yadav ,  David J. Icove

IPC分类号： G06F17/00

CPC分类号： G06F17/30327 ,  G06F17/30333 ,  G06F17/30598 ,  G06F19/24 ,  G06F19/28 ,  Y10S707/99932 ,  Y10S707/99933 ,  Y10S707/99935 ,  Y10S707/99942 ,  Y10S707/99945

摘要： A tree-structured index to multidimensional data is created using naturally occurring patterns and clusters within the data which permit efficient search and retrieval strategies in a database of DNA profiles. A search engine utilizes hierarchical decomposition of the database by identifying clusters of similar DNA profiles and maps to parallel computer architecture, allowing scale up past previously feasible limits. Key benefits of the new method are logarithmic scale up and parallelization. These benefits are achieved by identification and utilization of naturally occurring patterns and clusters within stored data. The patterns and clusters enable the stored data to be partitioned into subsets of roughly equal size. The method can be applied recursively, resulting in a database tree that is balanced, meaning that all paths or branches through the tree have roughly the same length. The method achieves high performance by exploiting the natural structure of the data in a manner that maintains balanced trees. Implementation of the method maps naturally to parallel computer architectures, allowing scale up to very large databases.

摘要翻译： 使用数据中的自然发生的模式和集群创建树形结构的多维数据索引，这些数据允许DNA简档数据库中的高效搜索和检索策略。 搜索引擎利用数据库的分层分解，通过识别类似DNA分布的集群并将其映射到并行计算机体系结构，从而超越以前可行的限制。 新方法的主要优点是对数放大和并行化。 这些优点通过识别和利用存储数据中的自然发生的模式和集群来实现。 模式和集群使存储的数据能够被划分成大致相等大小的子集。 该方法可以递归地应用，导致数据库树是平衡的，意味着通过树的所有路径或分支具有大致相同的长度。 该方法通过以保持平衡树的方式利用数据的自然结构来实现高性能。 该方法的实现自然映射到并行计算机体系结构，允许扩展到非常大的数据库。

公开(公告)号：US20080134195A1

公开(公告)日：2008-06-05

申请号：US11968367

申请日：2008-01-02

申请人： John D. Birdwell ,  Tse-Wei Wang ,  Roger D. Horn ,  Puneet Yadav ,  David J. Icove

发明人： John D. Birdwell ,  Tse-Wei Wang ,  Roger D. Horn ,  Puneet Yadav ,  David J. Icove

IPC分类号： G06F9/46 ,  G06F17/30

CPC分类号： G06F17/30327 ,  G06F17/30333 ,  G06F17/30598 ,  G06F19/24 ,  G06F19/28 ,  Y10S707/99932 ,  Y10S707/99933 ,  Y10S707/99935 ,  Y10S707/99942 ,  Y10S707/99945

摘要： A tree-structured index to multidimensional data is created using naturally occurring patterns and clusters within the data which permit efficient search and retrieval strategies in a database of DNA profiles. A search engine utilizes hierarchical decomposition of the database by identifying clusters of similar DNA profiles and maps to parallel computer architecture, allowing scale up past to previously feasible limits. Key benefits of the new method are logarithmic scale up and parallelization. These benefits are achieved by identification and utilization of naturally occurring patterns and clusters within stored data. The patterns and clusters enable the stored data to be partitioned into subsets of roughly equal size. The method can be applied recursively, resulting in a database tree that is balanced, meaning that all paths or branches through the tree have roughly the same length. The method achieves high performance by exploiting the natural structure of the data in a manner that maintains balanced trees. Implementation of the method maps naturally to parallel computer architectures, allowing scale up to very large databases.