Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed are a method of and system for detecting a consensus motif in a data sequence. The method comprises the steps of obtaining the data sequence, identifying potential signal (PS) segments of interest in the data sequence, and carrying out comparison and alignment processes amongst the PS segments to extract the consensus motif. Preferably, an unsupervised motif discovery process is used to identify the PS segments. More specifically, this may be done by extracting all common motifs across the sequence using the unsupervised motif discovery process; and for each of at least selected positions in the sequence, computing the weighted sum of the common motifs that cover said position. The PS segments that cover the positions where said number is above a given threshold may then be identified as the PS segments.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 j1, . . . , jy on the simulated chromosome are selected. A crossover event is placed at one or more of the positions j1, . . . , jy based on Y>0. An additional number Y? of positions j?1, . . . , j?y to be selected on the simulated chromosome is determined. Y? additional positions j?1, . . . , j?y on the simulated chromosome are selected. An additional crossover event is placed at one or more of the additional positions j?1, . . . , j?y based on Y?>0 and a neighborhood t associated with the one or more of the additional positions j?1, . . . , j?y being free of crossover events. A set of crossover event locations is identified based on the one or more of the positions j1, . . . , jy and additional positions j?1, . . . , j?y at which a crossover event has been placed.

Abstract: 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.

Abstract: 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.

Abstract: 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 j1, . . . , jy on the simulated chromosome are selected. A crossover event is placed at one or more of the positions j1, . . . , jy based on Y>0. An additional number Y? of positions j?1, . . . , j?y to be selected on the simulated chromosome is determined. Y? additional positions j?1, . . . , j?y on the simulated chromosome are selected. An additional crossover event is placed at one or more of the additional positions j?1, . . . , j?y based on Y?>0 and a neighborhood t associated with the one or more of the additional positions j?1, . . . , j?y being free of crossover events. A set of crossover event locations is identified based on the one or more of the positions j1, . . . , jy and additional positions j?1, . . . , j?y at which a crossover event has been placed.

Abstract: 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.

Abstract: 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.