Abstract: Method and apparatus for predicting properties of a target object, in particular, one of an origin and a source, comprise application of a search manager for analyzing parameters of a plurality of databases for a plurality of objects, the databases comprising an electrical, electromagnetic, acoustic spectral database (ESD), a micro-body assemblage database (MAD) and a database of image data whereby the databases store data objects containing identifying features, source information and information on site properties and context including time and frequency varying data. The method comprises application of multivariate statistical analysis and principal component analysis in combination with content-based image retrieval for providing two-dimensional attributes of three dimensional objects, for example, via preferential image segmentation using a tree of shapes and to predict further properties of objects by means of k-means clustering and related methods.

Abstract: Method and apparatus for predicting properties of a target object comprise application of a search manager for analyzing parameters of a plurality of databases for a plurality of objects, the databases comprising an electrical, electromagnetic, acoustic spectral database (ESD), a micro-body assemblage database (MAD) and a database of image data whereby the databases store data objects containing identifying features, source information and information on site properties and context including time and frequency varying data. The method comprises application of multivariate statistical analysis and principal component analysis in combination with content-based image retrieval for providing two-dimensional attributes of three dimensional objects, for example, via preferential image segmentation using a tree of shapes and to predict further properties of objects by means of k-means clustering and related methods.

Abstract: 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.

Abstract: Methods of analyzing data are provided. An expert system receives input from at least a first source. Data is imported and analyzed by an expert system, wherein the expert system makes at least one first decision, which characterizes the data based on a rule base. The at least one first decision is displayable and modifiable by a first input from a first source. In response to the first input from the first source, the rule base may be re-applied to make at least one second decision, wherein the at least one second decision is different from the at least one first decision, or the at least one first decision may be accepted. The at least one first decision or the at least one second decision is then displayable and modifiable in response to a first input from a second source.

Abstract: 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.

Abstract: 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.

Abstract: Three methods of predicting whether an unknown biological specimen of a missing person originates from a member of a particular family comprise an initial automated decision support (ADS) algorithm for determining a list of relatives of the missing person for DNA typing and which typing technologies of available technologies to use for a listed relative. The ADS algorithm may be implemented on computer apparatus including a processor and an associated memory. The ADS method comprises determining a set of relatives of available family member relatives for DNA typing via a processor from a stored list of family member relatives according to one of a rule base, a table of hierarchically stored relatives developed based on discriminatory power or by calculating the discriminatory power for available family relatives to type.

Abstract: Methods of analyzing data are provided. An expert system receives input from at least a first source. Data is imported and analyzed by an expert system, wherein the expert system makes at least one first decision, which characterizes the data based on a rule base. The at least one first decision is displayable and modifiable by a first input from a first source. In response to the first input from the first source, the rule base may be re-applied to make at least one second decision, wherein the at least one second decision is different from the at least one first decision, or the at least one first decision may be accepted. The at least one first decision or the at least one second decision is then displayable and modifiable in response to a first input from a second source.

Abstract: 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.

Abstract: Methods of analyzing data are provided. An expert system receives input from at least a first source. Data is imported and analyzed by an expert system, wherein the expert system makes at least one first decision, which characterizes the data based on a rule base. The at least one first decision is displayable and modifiable by a first input from a first source. In response to the first input from the first source, the rule base may be re-applied to make at least one second decision, wherein the at least one second decision is different from the at least one first decision, or the at least one first decision may be accepted. The at least one first decision or the at least one second decision is then displayable and modifiable in response to a first input from a second source.

Abstract: An item is organized and presented by displaying a table, wherein the table displays a plurality of data comprising analysis results characterizing the at least one item, and wherein an analysis result is based on a decision made by an expert system according to a rule base. Input is accepted from a source, wherein the input may cause the analysis results to be modified, and wherein the results may be modified by re-applying the rule base. In response to the input, an updated table is created, wherein the updated table comprises the modified analysis results. This updated table is then displayable.

Abstract: Methods of analyzing data are provided. An expert system receives input from at least a first source. Data is imported and analyzed by an expert system, wherein the expert system makes at least one first decision, which characterizes the data based on a rule base. The at least one first decision is displayable and modifiable by a first input from a first source. In response to the first input from the first source, the rule base may be re-applied to make at least one second decision, wherein the at least one second decision is different from the at least one first decision, or the at least one first decision may be accepted. The at least one first decision or the at least one second decision is then displayable and modifiable in response to a first input from a second source.

Abstract: Analysis of DNA is critical to many applications including identifying perpetrators of crimes based on genetic evidence left at crime scenes. An initial step to analyzing DNA data is detection, identification, and quantization of allele peaks in the DNA data. The invention provides a method and apparatus for accurately and expeditiously performing this initial step by sequentially checking unfitted peaks against various models including a default model, a hybrid peak model, a dual fit model and, in special situations, a narrow fit function and a saturated fit function.

Abstract: Methods of analyzing data are provided. An expert system receives input from at least a first source. Data is imported and analyzed by an expert system, wherein the expert system makes at least one first decision, which characterizes the data based on a rule base. The at least one first decision is displayable and modifiable by a first input from a first source. In response to the first input from the first source, the rule base may be re-applied to make at least one second decision, wherein the at least one second decision is different from the at least one first decision, or the at least one first decision may be accepted. The at least one first decision or the at least one second decision is then displayable and modifiable in response to a first input from a second source.

Abstract: 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.

Abstract: Methods of analyzing data are provided. An expert system receives input from at least a first source. Data is imported and analyzed by an expert system, wherein the expert system makes at least one first decision, which characterizes the data based on a rule base. The at least one first decision is displayable and modifiable by a first input from a first source. In response to the first input from the first source, the rule base may be re-applied to make at least one second decision, wherein the at least one second decision is different from the at least one first decision, or the at least one first decision may be accepted. The at least one first decision or the at least one second decision is then displayable and modifiable in response to a first input from a second source.

Abstract: 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.

Abstract: An item is organized and presented by displaying a table, wherein the table displays a plurality of data comprising analysis results characterizing the at least one item, and wherein an analysis result is based on a decision made by an expert system according to a rule base. Input is accepted from a source, wherein the input may cause the analysis results to be modified, and wherein the results may be modified by re-applying the rule base. In response to the input, an updated table is created, wherein the updated table comprises the modified analysis results. This updated table is then displayable.

Abstract: 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.

Abstract: 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.