Abstract: Generally, the present invention applies a transformation to convert a probability distribution of gene expression signals in control samples to a uniform distribution. The uniform distribution allows better comparisons between expression levels for genes. The transformation is derived from gene expression signals of control data, and is applied to gene expression signals of phenotype data. The phenotype data can be represented in a matrix format. A number of gene expression patterns may be determined (in the form of submatrices) that will characterize the phenotype. The uniform distribution helps in this regard, as it allows better comparisons of patterns. The gene expression patterns can then be used to classify samples as belonging to the phenotype set. Preferably, a discriminant function is used to compare a sample with the gene expression patterns that characterize the phenotype. The discriminant function can determine a score that can be used to determine whether the sample belongs to the phenotype.

Abstract: Generally, the present invention applies a transformation to convert a probability distribution of gene expression signals in control samples to a uniform distribution. The uniform distribution allows better comparisons between expression levels for genes. The transformation is derived from gene expression signals of control data, and is applied to gene expression signals of phenotype data. The phenotype data can be represented in a matrix format. A number of gene expression patterns may be determined (in the form of submatrices) that will characterize the phenotype. The uniform distribution helps in this regard, as it allows better comparisons of patterns. The gene expression patterns can then be used to classify samples as belonging to the phenotype set. Preferably, a discriminant function is used to compare a sample with the gene expression patterns that characterize the phenotype. The discriminant function can determine a score that can be used to determine whether the sample belongs to the phenotype.

Abstract: Techniques for reconstructing networks are provided. In one aspect, a method for reconstructing a synthetic network, such as a synthetic biological network, is provided. In another aspect, a method for reconstructing a supply chain network is provided. Exemplary supply chain networks include supply chains for petroleum distribution.

Abstract: Techniques for reconstructing networks are provided. In one aspect, a method for reconstructing a synthetic network, such as a synthetic biological network, is provided. In another aspect, a method for reconstructing a supply chain network is provided. Exemplary supply chain networks include supply chains for petroleum distribution.

Abstract: Techniques for reconstructing networks are provided. In one aspect, a method for reconstructing a synthetic network, such as a synthetic biological network, is provided. In another aspect, a method for reconstructing a supply chain network is provided. Exemplary supply chain networks include supply chains for petroleum distribution.

Abstract: Techniques for reconstructing networks are provided. In one aspect, a method for reconstructing a synthetic network, such as a synthetic biological network, is provided. In another aspect, a method for reconstructing a supply chain network is provided. Exemplary supply chain networks include supply chains for petroleum distribution.

Abstract: Techniques for reconstructing networks are provided. In one aspect, a method for reconstructing a synthetic network, such as a synthetic biological network, is provided. In another aspect, a method for reconstructing a supply chain network is provided. Exemplary supply chain networks include supply chains for petroleum distribution.

Abstract: Techniques for reconstructing networks are provided. In one aspect, a method for reconstructing a synthetic network, such as a synthetic biological network, is provided. In another aspect, a method for reconstructing a supply chain network is provided. Exemplary supply chain networks include supply chains for petroleum distribution.

Abstract: Techniques for analyzing gene expression levels are provided. In one aspect of the invention, the technique provides a method for determining a concentration level of a target nucleic acid, the target nucleic acid comprising at least one target oligonucleotide. The method determines (i) a measure of affinity value of the target oligonucleotide with a probe oligonucleotide; and (ii) a hybridization intensity value for the target oligonucleotide and the probe oligonucleotide at a probe spot. The measure of affinity value and the hybridization intensity value are used to determine the concentration level of the target nucleic acid.

Abstract: Techniques for analyzing gene expression levels are provided. In one aspect of the invention, the technique provides a method for determining a concentration level of a target nucleic acid, the target nucleic acid comprising at least one target oligonucleotide. The method determines (i) a measure of affinity value of the target oligonucleotide with a probe oligonucleotide; and (ii) a hybridization intensity value for the target oligonucleotide and the probe oligonucleotide at a probe spot. The measure of affinity value and the hybridization intensity value are used to determine the concentration level of the target nucleic acid.

Abstract: Generally, the present invention applies a transformation to convert a probability distribution of gene expression signals in control samples to a uniform distribution. The uniform distribution allows better comparisons between expression levels for genes. The transformation is derived from gene expression signals of control data, and is applied to gene expression signals of phenotype data. The phenotype data can be represented in a matrix format. A number of gene expression patterns may be determined (in the form of submatrices) that will characterize the phenotype. The uniform distribution helps in this regard, as it allows better comparisons of patterns. The gene expression patterns can then be used to classify samples as belonging to the phenotype set. Preferably, a discriminant function is used to compare a sample with the gene expression patterns that characterize the phenotype. The discriminant function can determine a score that can be used to determine whether the sample belongs to the phenotype.