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 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: Embodiments of the present invention include method, systems and computer program products for algebraic phasing of polyploids. Aspects of the invention include receiving a matrix including a set of two or more single-nucleotide poloymorphisms (SNPs) for two or more sample organisms. Each row of the matrix is set to a ploidy based on a number of ploidies present in the two or more sample organisms. Each allele in the set of two or more SNPs is represented as a binary number. A set of algebraic rules is received, wherein the set of algebraic rules include an algebraic phasing algorithm. And the set of algebraic rules are applied to the matrix to determine a haplotype of a parent of the two or more sample organisms.

Abstract: Embodiments of the present invention include method, systems and computer program products for algebraic phasing of polyploids. Aspects of the invention include receiving a matrix including a set of two or more single-nucleotide poloymorphisms (SNPs) for two or more sample organisms. Each row of the matrix is set to a ploidy based on a number of ploidies present in the two or more sample organisms. Each allele in the set of two or more SNPs is represented as a binary number. A set of algebraic rules is received, wherein the set of algebraic rules include an algebraic phasing algorithm. And the set of algebraic rules are applied to the matrix to determine a haplotype of a parent of the two or more sample organisms.

Abstract: A semiconductor structure and a method for fabricating the same. The semiconductor structure includes a gate cut mask having one cut window exposing one or more portions of multiple sacrificial gate structures of the at least one plurality of sacrificial gate structures. The multiple sacrificial gate structures having been formed over portions of in structures. The method comprises forming a gate cut mask a plurality of semiconductor fins and a plurality of sacrificial gate structures. The gate cut mask being formed with one cut window exposing one or more portions of multiple sacrificial gate structures of the plurality of sacrificial gate structures. At least the portion of multiple sacrificial gate structures and one or more portions of each semiconductor fin of the plurality of semiconductor fins underlying the one or more portions of one of the multiple sacrificial gate structures are removed.

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: Embodiments of the present invention are directed to a computer-implemented method for positive OTU identification. A non-limiting example of the computer-implemented method includes receiving, by a processor, a plurality of sequencing reads for a metagenome sample and, for each of the plurality of sequencing reads, a corresponding OTU set comprising a plurality of OTUs. The method also includes determining, by the processor, a true positive score for each of the plurality of OTUs based upon a ?ech Complex and generating a plurality of preliminary OTUs. The method also includes determining a threshold score for the preliminary OTUs. The method also includes removing one of the preliminary OTUs based at least in part upon a determination that the true positive score is less than a threshold. The method also includes retaining one of the preliminary OTUs based at least in part upon a determination that the true positive score is greater than or equal to the threshold.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for positive OTU identification. A non-limiting example of the computer-implemented method includes receiving, by a processor, a plurality of sequencing reads for a metagenome sample and, for each of the plurality of sequencing reads, a corresponding OTU set comprising a plurality of OTUs. The method also includes determining, by the processor, a true positive score for each of the plurality of OTUs based upon a C?ech Complex and generating a plurality of preliminary OTUs. The method also includes determining a threshold score for the preliminary OTUs. The method also includes removing one of the preliminary OTUs based at least in part upon a determination that the true positive score is less than a threshold. The method also includes retaining one of the preliminary OTUs based at least in part upon a determination that the true positive score is greater than or equal to the threshold.

Abstract: Embodiments of the present invention include method, systems and computer program products for algebraic phasing of polyploids. Aspects of the invention include receiving a matrix including a set of two or more single-nucleotide polymorphisms (SNPs) for two or more sample organisms. Each row of the matrix is set to a ploidy based on a number of ploidies present in the two or more sample organisms. Each allele in the set of two or more SNPs is represented as a binary number. A set of algebraic rules is received, wherein the set of algebraic rules include an algebraic phasing algorithm. And the set of algebraic rules are applied to the matrix to determine a haplotype of a parent of the two or more sample organisms.

Abstract: Embodiments of the present invention include method, systems and computer program products for algebraic phasing of polyploids. Aspects of the invention include receiving a matrix including a set of two or more single-nucleotide poloymorphisms (SNPs) for two or more sample organisms. Each row of the matrix is set to a ploidy based on a number of ploidies present in the two or more sample organisms. Each allele in the set of two or more SNPs is represented as a binary number. A set of algebraic rules is received, wherein the set of algebraic rules include an algebraic phasing algorithm. And the set of algebraic rules are applied to the matrix to determine a haplotype of a parent of the two or more sample organisms.

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 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 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: A method includes generating, by a processor system, a graph. The graph is based at least in part on a plurality of instances in which operational taxonomic units are identified as being represented within an environment. The method can also include determining, using the processor system, that at least one instance of the plurality of instances corresponds to a false-positive identification of an operational taxonomic unit. The determining is based on the properties of the graph. The method can also include reporting the determination.

Abstract: A nanodevice includes a nanochannel disposed through a dielectric material. A first electrode is disposed on a first side of the nanochannel, is formed within the dielectric material and has a surface exposed within the nanochannel. A second electrode is disposed on a second side of the nanochannel, is formed within the dielectric material and has a surface exposed within the nanochannel opposite the first electrode. A power circuit is connected between the first and second electrodes to create a potential difference between the first and second electrodes such that portions of a molecule can be identified by a change in electrical properties across the first and second electrodes as the molecule passes.

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: Embodiments are directed to a computer-based system for analyzing genotype data of a set of multiple progeny to estimate information about unknown parents of the multiple progeny. The system includes a memory and a processor system communicatively coupled to the memory. The processor system is configured to receive data representing markers of each genotype of each of the multiple progeny, compare the data to identify compatible genotypes having compatible markers, and label the compatible genotypes as having at least one parent in common.

Abstract: Embodiments are directed to a computer-based system for analyzing genotype data of a set of multiple progeny to estimate information about unknown parents of the multiple progeny. The system includes a memory and a processor system communicatively coupled to the memory. The processor system is configured to receive data representing markers of each genotype of each of the multiple progeny, compare the data to identify compatible genotypes having compatible markers, and label the compatible genotypes as having at least one parent in common.

Abstract: Embodiments are directed to a computer-based simulation system including an input circuit, a memory and a processor system communicatively coupled to the memory and the input circuit. The input circuit is configured to receive an input distribution. The processor system is configured to assign, for each marker of a simulated population matrix, a minor allele frequency. The processor system is further configured to assign, for each marker and each distance of the simulated population matrix, a linkage disequilibrium.