Patent application number | Description | Published |
20080250002 | OBJECT CLASSIFICATION - Classification of objects using the best boolean expression that represents the most optimal combination of the underlying features is disclosed. | 10-09-2008 |
20090030912 | SIGNIFICANCE MEASURE OF STRUCTURED CLUSTERS - The exemplary embodiments provide a computer implemented method, apparatus, and computer usable program code for calculating a probability. An input is received, wherein the input comprises a PQ tree. The leaf nodes of the PQ tree are counted to form a number of leaf nodes. A factorial value of the number of leaf nodes is calculated to form a denominator. A hash value of a frontier of all permutations of the PQ tree is calculated to form a numerator. A ratio of the numerator to the denominator is determined to form a result. The result is displayed to a user. | 01-29-2009 |
20090132584 | METHOD FOR RECONSTRUCTING EVOLUTIONARY DATA - Techniques for reconstructing evolutionary data of a set of genomic data are provided. The techniques include obtaining a set of genomic data, determining a chronological order of one or more mutations within the set of genomic data, determining a chronological order of one or more recombinations within the set of genomic data, determining a position of each recombination within the set of genomic data, and combining the chronological order of the one or more mutations, the chronological order of the one or more recombinations and the position of each recombination to reconstruct evolutionary data of the set of genomic data. | 05-21-2009 |
20090259626 | METHOD AND SYSTEM FOR IDENTIFYING PARTIAL ORDER PATTERNS IN SEQUENCES OF DATA - A method and system are disclosed for identifying partial order patterns of a set of motifs in a data sequence. The method comprises the steps of obtaining the data sequence, identifying a set of motifs in the data sequence, identifying a plurality of partial orders of the motifs in the data sequence, and using the identified partial orders to identify functions of the motifs. In the preferred embodiment of the invention, the step of identifying the plurality of partial orders of the motifs includes the step of converting the identified motifs to an (n×m) incidence matrix, I, of expressions. Also, in this preferred embodiment, the step of identifying the plurality of partial orders of the motifs includes the steps of computing a partial order description of each of said expressions, and computing a redescription of each of said partial order descriptions. | 10-15-2009 |
20100049449 | Pattern Discovery Techniques for Determining Maximal Irredundant and Redundant Motifs - Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs, until all basis motifs have been selected. | 02-25-2010 |
20100049685 | Pattern Discovery Techniques for Determining Maximal Irredundant and Redundant Motifs - Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs, until all basis motifs have been selected. | 02-25-2010 |
20100057373 | Pattern Discovery Techniques for Determining Maximal Irredundant and Redundant Motifs - Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs are formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs. This process continues, by selecting additional basis motifs, until all basis motifs have been selected. | 03-04-2010 |
20120109867 | Pattern Discovery Techniques for Determining Maximal Irredundant and Redundant Motifs - Basis motifs are determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care characters and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs that are formed by determining sets for selected basis motifs. From these sets, unique intersection sets are determined. This process continues, by selecting additional basis motifs, until all basis motifs have been selected. An apparatus for performing the process is also disclosed. | 05-03-2012 |
20120191356 | Assembly Error Detection - A method for detecting errors in genetic sequence assemblies including defining an assembly (A) of a sequence of genetic data, collecting read data into a library of reads (L), plotting histograms of sizes or reads versus a number of reads per size, normalizing a distribution (D) with a coverage C to obtain D′ that has a mean (μ) and standard deviation (σ) and reserve positions (i) not used to obtain D′, collecting subset of reads (S | 07-26-2012 |
20120330563 | Assembly Error Detection - A method for detecting errors in genetic sequence assemblies including defining an assembly (A) of a sequence of genetic data, collecting read data into a library of reads (L), plotting histograms of sizes or reads versus a number of reads per size, normalizing a distribution (D) with a coverage C to obtain D′ that has a mean (μ) and standard deviation (σ) and reserve positions (i) not used to obtain D′, collecting subset of reads (S | 12-27-2012 |
20120331008 | SAMPLING THE SPACE OF ANCESTRAL RECOMBINATION GRAPHS - A method is provided for constructing an ancestral recombination graph. A value K is received representing K extant units. M non-mixing segments are also received. K vertices V are generated. K lineages for each of M trees are associated with each of the K vertices. An ancestral recombination graph is constructed. To construct the ancestral recombination graph, there is repeated, until only one lineage survives for each of the M trees, a process that includes the following. A tree is randomly selected tree. A first vertex v | 12-27-2012 |
20130289890 | Rank Normalization for Differential Expression Analysis of Transcriptome Sequencing Data - A computer-implemented method for rank normalization for differential expression analysis of transcriptome sequencing data includes receiving, by a computer, a first dataset comprising transcriptome sequencing data, the first dataset comprising a plurality of genes, and further comprising a respective ranking value associated with each of the plurality of genes; assigning a rank to each of the genes of the plurality of genes based on the ranking value to produce a first rank normalized dataset; determining a change between a first rank of a particular gene in the first rank normalized dataset, and a second rank of the particular gene in a second rank normalized dataset, the second rank normalized dataset being based on a second dataset comprising transcriptome sequencing data; and determining whether the particular gene is differentially expressed between the first dataset and the second dataset based on the determined change in rank. | 10-31-2013 |
20130289891 | Rank Normalization for Differential Expression Analysis of Transcriptome Sequencing Data - A computer system for rank normalization for differential expression analysis of transcriptome sequencing data includes a processor; and a memory comprising a first dataset comprising transcriptome sequencing data, the first dataset comprising a plurality of genes and a respective ranking value associated with each of the plurality of genes, the system configured to perform a method including assigning a rank to each of the genes of the plurality of genes based on the ranking value to produce a first rank normalized dataset; determining a change between a first rank of a particular gene in the first rank normalized dataset, and a second rank of the particular gene in a second rank normalized dataset, the second rank normalized dataset being based on a second dataset comprising transcriptome sequencing data; and determining whether the particular gene is differentially expressed between the first and second datasets based on the determined change in rank. | 10-31-2013 |
20130345986 | EXACT HAPLOTYPE RECONSTRICTION OF F2 POPULATIONS - Various embodiments reconstruct haplotypes from genotype data. In one embodiment, a set of progeny genotype data comprising n progenies encoded with m genetic markers is accessed. A first set of parent haplotypes associated with a first parent of the n progenies and a second set of parent haplotypes associated with a second parent of the n progenies are identified based on at least the set of progeny genotype data. A total minimum number of observable crossovers in the n progenies is determined. An agglomerate data structure comprising a collection of sets of haplotype sequences characterizing the n progenies is constructed based on the set of progeny genotype data and the first and second sets of parent haplotypes. Each set of haplotype sequences includes a number of crossovers equal to the total minimum number of observable crossovers in the n progenies. | 12-26-2013 |
20130345987 | EXACT HAPLOTYPE RECONSTRICTION OF F2 POPULATIONS - A system for reconstructing haplotypes from genotype data includes a memory, a processor, and a reconstruction module. The reconstruction module is configured to access a set of progeny genotype data including n progenies encoded with m genetic markers. A first set of parent haplotypes associated with a first parent of the n progenies and a second set of parent haplotypes associated with a second parent of the n progenies are identified based on at least the set of progeny genotype data. An agglomerate data structure including a collection of sets of haplotype sequences characterizing the n progenies is constructed based on the set of progeny genotype data and the first and second sets of parent haplotypes. Each set of haplotype sequences includes a number of crossovers equal to a total minimum number of observable crossovers in the n progenies. | 12-26-2013 |
20140136160 | QUANTITATIVE MODELS OF MULTI-ALLELIC MULTI-LOCI INTERACTIONS - Various embodiments generate a quantitative model of multi-allelic multi-loci interactions. In one embodiment, a plurality of distinct allelic forms of at least two loci of an entity is received. Each of the plurality of distinct allelic forms is associated with a set of genotypes. A contribution value of each genotype to a given physical trait is determined for each set of genotypes. An interaction contribution value for each interaction between each of the set of genotypes of a first of the least two loci and each of the set of genotypes of at least a second of the least two loci to the physical trait is determined from at least one interaction model. A model of a quantitative value of the entity is generated based on the contribution value of each genotype in each set of genotypes and each interaction contribution value that has been determined from the interaction model. | 05-15-2014 |
20140136161 | PRECISE SIMULATION OF PROGENY DERIVED FROM RECOMBINING PARENTS - Various embodiments simulate crossover events on a chromosome. In one embodiment, a number Y of positions to be selected on a simulated chromosome is determined. Y positions j | 05-15-2014 |
20140136166 | PRECISE SIMULATION OF PROGENY DERIVED FROM RECOMBINING PARENTS - Various embodiments simulate crossover events on a chromosome. In one embodiment, a number Y of positions to be selected on a simulated chromosome is determined. Y positions j | 05-15-2014 |
20140136167 | QUANTITATIVE MODELS OF MULTI-ALLELIC MULTI-LOCI INTERACTIONS - Various embodiments generate a quantitative model of multi-allelic multi-loci interactions. In one embodiment, a plurality of distinct allelic forms of at least two loci of an entity is received. Each of the plurality of distinct allelic forms is associated with a set of genotypes. A contribution value of each genotype to a given physical trait is determined for each set of genotypes. An interaction contribution value for each interaction between each of the set of genotypes of a first of the least two loci and each of the set of genotypes of at least a second of the least two loci to the physical trait is determined from at least one interaction model. A model of a quantitative value of the entity is generated based on the contribution value of each genotype in each set of genotypes and each interaction contribution value that has been determined from the interaction model. | 05-15-2014 |
20140156235 | MODELING MULTIPLE INTERACTIONS BETWEEN MULTIPLE LOCI - Various embodiments generate a quantitative model of genetic effect. In one embodiment, a processor receives a set of loci of an entity. Each locus is associated with a contribution value to a given physical trait. A first set of interacting loci associated with a first interaction and at least a second set of interacting loci associated with at least a second interaction are identified. The first interaction type is associated with a first interaction model. The at least the second interaction is associated at least a second interaction model. A model of a quantitative value of the entity is generated based on at least the contribution value associated with each locus in the set of loci, a contribution value of the first interaction as defined by the first interaction model, and a contribution value of the second interaction as defined by the at least the second interaction model. | 06-05-2014 |
20140156236 | MODELING MULTIPLE INTERACTIONS BETWEEN MULTIPLE LOCI - Various embodiments generate a quantitative model of genetic effect. In one embodiment, a processor receives a set of loci of an entity. Each locus is associated with a contribution value to a given physical trait. A first set of interacting loci associated with a first interaction and at least a second set of interacting loci associated with at least a second interaction are identified. The first interaction type is associated with a first interaction model. The at least the second interaction is associated at least a second interaction model. A model of a quantitative value of the entity is generated based on at least the contribution value associated with each locus in the set of loci, a contribution value of the first interaction as defined by the first interaction model, and a contribution value of the second interaction as defined by the at least the second interaction model. | 06-05-2014 |
20140164395 | EFFICIENT SORTING OF LARGE DIMENSIONAL DATA - Various embodiments sort data. In one embodiment, a matrix D including a set of data values is received. A matrix Q is received, and includes a set of columns and a set of rows. The matrix Q further includes a sorting of each column of the matrix D. Each of these rows corresponds to a sorting. Each of a set of values in each of the set of columns in the matrix Q identifies a row in the matrix D. At least one sub-matrix D′ of the matrix D is identified. A set of columns of the sub-matrix D′ is restricted to one or more columns of the matrix D. A processor sorts the sub-matrix D′ by rows based on the sorting of the set of columns of the matrix D as given in the matrix Q, and based on the set of data values in the matrix D. | 06-12-2014 |
20140164402 | EFFICIENT SORTING OF LARGE DIMENSIONAL DATA - Various embodiments sort data. In one embodiment, a matrix D including a set of data values is received. A matrix Q is received, and includes a set of columns and a set of rows. The matrix Q further includes a sorting of each column of the matrix D. Each of these rows corresponds to a sorting. Each of a set of values in each of the set of columns in the matrix Q identifies a row in the matrix D. At least one sub-matrix D′ of the matrix D is identified. A set of columns of the sub-matrix D′ is restricted to one or more columns of the matrix D. A processor sorts the sub-matrix D′ by rows based on the sorting of the set of columns of the matrix D as given in the matrix Q, and based on the set of data values in the matrix D. | 06-12-2014 |
20140172312 | STABLE GENES IN COMPARATIVE TRANSCRIPTOMICS - Various embodiments perform stable gene analysis of transcriptome sequencing data. In one embodiment, a plurality of datasets each including transcriptome sequencing data are received by a processor. Each of the plurality of datasets includes a plurality of genes and a respective ranking value for each of the plurality of genes. A plurality of rank normalized input datasets is generated based on assigning, for each of the plurality of datasets, a rank to each of the plurality of genes. One or more longest increasing subsequence (LIS) of ranks are identified between each pair of the plurality of rank normalized input datasets. A set of stable genes from the plurality of genes is identified based on each of the one or more LIS of ranks across the plurality of rank normalized input datasets. | 06-19-2014 |
20140172320 | STABLE GENES IN COMPARATIVE TRANSCRIPTOMICS - Various embodiments perform stable gene analysis of transcriptome sequencing data. In one embodiment, a plurality of datasets each including transcriptome sequencing data are received by a processor. Each of the plurality of datasets includes a plurality of genes and a respective ranking value for each of the plurality of genes. A plurality of rank normalized input datasets is generated based on assigning, for each of the plurality of datasets, a rank to each of the plurality of genes. One or more longest increasing subsequence (LIS) of ranks are identified between each pair of the plurality of rank normalized input datasets. A set of stable genes from the plurality of genes is identified based on each of the one or more LIS of ranks across the plurality of rank normalized input datasets. | 06-19-2014 |
20140207427 | FEATURE SELECTION FOR EFFICIENT EPISTASIS MODELING FOR PHENOTYPE PREDICTION - Various embodiments select markers for modeling epistasis effects. In one embodiment, a processor receives a set of genetic markers and a phenotype. A relevance score is determined with respect to the phenotype for each of the set of genetic markers. A threshold is set based on the relevance score of a genetic marker with a highest relevancy score. A relevance score is determined for at least one genetic marker in the set of genetic markers for at least one interaction between the at least one genetic marker and at least one other genetic marker in the set of genetic markers. The at least one interaction is added to a top-k feature set based on the relevance score of the at least one interaction satisfying the threshold. | 07-24-2014 |
20140207436 | FEATURE SELECTION FOR EFFICIENT EPISTASIS MODELING FOR PHENOTYPE PREDICTION - Various embodiments select markers for modeling epistasis effects. In one embodiment, a processor receives a set of genetic markers and a phenotype. A relevance score is determined with respect to the phenotype for each of the set of genetic markers. A threshold is set based on the relevance score of a genetic marker with a highest relevancy score. A relevance score is determined for at least one genetic marker in the set of genetic markers for at least one interaction between the at least one genetic marker and at least one other genetic marker in the set of genetic markers. The at least one interaction is added to a top-k feature set based on the relevance score of the at least one interaction satisfying the threshold. | 07-24-2014 |
20140207710 | TRANSDUCTIVE LASSO FOR HIGH-DIMENSIONAL DATA REGRESSION PROBLEMS - Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. A first centered Gram matrix of a given dimension is determined for each of a set of feature vectors that include at least one of the set of training samples and at least one of the set of test samples. A second centered Gram matrix of the given dimension is determined for a target value vector that includes target values from the set of training samples. A set of columns and rows associated with the at least one of the test samples in the second centered Gram matrix is set to 0. A subset of features is selected from a set of features based on the first and second centered Gram matrices. | 07-24-2014 |
20140207711 | TRANSDUCTIVE FEATURE SELECTION WITH MAXIMUM-RELEVANCY AND MINIMUM-REDUNDANCY CRITERIA - Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. The set of training samples includes a set of features and a class value. The set of test samples includes the set of features absent the class value. A relevancy with respect to the class value is determined for each of a plurality of unselected features based on the set of training samples. A redundancy with respect to one or more of the set of features is determined for each of the plurality of unselected features in the first set of features based on the set of training samples and the set of test samples. A set of features is selected from the plurality of unselected features based on the relevancy and the redundancy determined for each of the plurality of unselected features. | 07-24-2014 |
20140207713 | TRANSDUCTIVE FEATURE SELECTION WITH MAXIMUM-RELEVANCY AND MINIMUM-REDUNDANCY CRITERIA - Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. The set of training samples includes a set of features and a class value. The set of test samples includes the set of features absent the class value. A relevancy with respect to the class value is determined for each of a plurality of unselected features based on the set of training samples. A redundancy with respect to one or more of the set of features is determined for each of the plurality of unselected features in the first set of features based on the set of training samples and the set of test samples. A set of features is selected from the plurality of unselected features based on the relevancy and the redundancy determined for each of the plurality of unselected features. | 07-24-2014 |
20140207714 | TRANSDUCTIVE LASSO FOR HIGH-DIMENSIONAL DATA REGRESSION PROBLEMS - Various embodiments select features from a feature space. In one embodiment, a set of training samples and a set of test samples are received. A first centered Gram matrix of a given dimension is determined for each of a set of feature vectors that include at least one of the set of training samples and at least one of the set of test samples. A second centered Gram matrix of the given dimension is determined for a target value vector that includes target values from the set of training samples. A set of columns and rows associated with the at least one of the test samples in the second centered Gram matrix is set to 0. A subset of features is selected from a set of features based on the first and second centered Gram matrices. | 07-24-2014 |
20140207764 | DYNAMIC FEATURE SELECTION WITH MAX-RELEVANCY AND MINIMUM REDUNDANCY CRITERIA - Various embodiments select features from a feature space. In one embodiment a set of features and a class value are received. A redundancy score is obtained for a feature that was previously selected from the set of features. A redundancy score is determined, for each of a plurality of unselected features in the set of features, based on the redundancy score that has been obtained, and a redundancy between the unselected feature and the feature that was previously selected. A relevance to the class value is determined for each of the unselected features. A feature from the plurality of unselected features with a highest relevance to the class value and a lowest redundancy score is selected. | 07-24-2014 |
20140207765 | DYNAMIC FEATURE SELECTION WITH MAX-RELEVANCY AND MINIMUM REDUNDANCY CRITERIA - Various embodiments select features from a feature space. In one embodiment a set of features and a class value are received. A redundancy score is obtained for a feature that was previously selected from the set of features. A redundancy score is determined, for each of a plurality of unselected features in the set of features, based on the redundancy score that has been obtained, and a redundancy between the unselected feature and the feature that was previously selected. A relevance to the class value is determined for each of the unselected features. A feature from the plurality of unselected features with a highest relevance to the class value and a lowest redundancy score is selected. | 07-24-2014 |
20140207799 | HILL-CLIMBING FEATURE SELECTION WITH MAX-RELEVANCY AND MINIMUM REDUNDANCY CRITERIA - Various embodiments select features from a feature space. In one embodiment a candidate feature set of k′ features is selected from at least one set of features based on maximum relevancy and minimum redundancy (MRMR) criteria. A target feature set of k features is identified from the candidate feature set, where k′>k. Each a plurality of features in the target feature set is iteratively updated with each of a plurality of k′−k features from the candidate feature set. The feature from the plurality of k′−k features is maintained in the target feature set, for at least one iterative update, based on a current MRMR score of the target feature set satisfying a threshold. The target feature set is stored as a top-k feature set of the at least one set of features after a given number of iterative updates. | 07-24-2014 |
20140207800 | HILL-CLIMBING FEATURE SELECTION WITH MAX-RELEVANCY AND MINIMUM REDUNDANCY CRITERIA - Various embodiments select features from a feature space. In one embodiment a candidate feature set of k′ features is selected from at least one set of features based on maximum relevancy and minimum redundancy (MRMR) criteria. A target feature set of k features is identified from the candidate feature set, where k′>k. Each a plurality of features in the target feature set is iteratively updated with each of a plurality of k′−k features from the candidate feature set. The feature from the plurality of k′−k features is maintained in the target feature set, for at least one iterative update, based on a current MRMR score of the target feature set satisfying a threshold. The target feature set is stored as a top-k feature set of the at least one set of features after a given number of iterative updates. | 07-24-2014 |
20150061903 | LOSSLESS COMPRESSION OF THE ENUMERATION SPACE OF FOUNDER LINE CROSSES - Various embodiments provide lossless compression of an enumeration space for genetic founder lines. In one embodiment, an input comprising a set of genetic founder lines and a maximum number of generations G is obtained. A set of genetic crossing templates of a height h is generated. A determination is made if at least a first genetic crossing template in the set of genetic crossing templates is redundant with respect to a second genetic crossing template in the set of genetic crossing templates. Based on the at least first genetic crossing template being redundant is redundant with respect to the second genetic crossing template, the at least first genetic crossing template is removed from the set of genetic crossing templates. This process of removing the at least first genetic crossing template from the set of genetic crossing templates the redundant creates an updated set of genetic crossing templates. | 03-05-2015 |
20150065361 | LOSSLESS COMPRESSION OF THE ENUMERATION SPACE OF FOUNDER LINE CROSSES - Various embodiments provide lossless compression of an enumeration space for genetic founder lines. In one embodiment, an input comprising a set of genetic founder lines and a maximum number of generations G is obtained. A set of genetic crossing templates of a height h is generated. A determination is made if at least a first genetic crossing template in the set of genetic crossing templates is redundant with respect to a second genetic crossing template in the set of genetic crossing templates. Based on the at least first genetic crossing template being redundant is redundant with respect to the second genetic crossing template, the at least first genetic crossing template is removed from the set of genetic crossing templates. This process of removing the at least first genetic crossing template from the set of genetic crossing templates the redundant creates an updated set of genetic crossing templates. | 03-05-2015 |