Abstract: A method for obtaining a candidate nucleotide sequence S indicative of a sequence of a target polynucleotide molecule that produces a hybridization signal I({right arrow over (x)}) upon incubation with a polynucleotide {right arrow over (x)} for each polynucleotide {right arrow over (x)} in a set E of polynucleotides. For each polynucleotide {right arrow over (x)} in the set E of polynucleotides, a probability P0({right arrow over (x)}) of the hybridization signal I({right arrow over (x)}) when the sequence {right arrow over (x)} is not complementary to a subsequence of T and a probability P1({right arrow over (x)}) of the hybridization signal when the sequence {right arrow over (x)} is complementary to a subsequence of T are obtained; so as to obtain a probabilistic spectrum (PS) of T. A score is then assigned to each of a plurality of candidate nucleotide sequences that is being based upon the probabilistic spectrum and upon a reference nucleotide sequence H.

Abstract: A method for obtaining a nucleotide sequence that is indicative of the sequence of a target polynucleotide molecule T. The method makes use of hybridization data obtained by incubating T with nucleotide probes. A score is assigned to each of a plurality of candidate nucleotide sequences based upon the hybridization data and upon at least one reference nucleotide sequence. A candidate nucleotide sequence in then selected having an essentially maximal score.

Abstract: A method of classifying a plurality of elements, such as genes, an associated system and an associated computer-read-able storage medium. Similarity values for pairs of elements are measured. For example, in the case of genes, gene expression fingerprints are measured, and the similarity values are computed from the fingerprints. A graph is constructed such that each vertex of the graph corresponds to a respective element. Each edge of the graph is assigned a superbinary weight that is based on the corresponding similarity value. The graph is partitioned into kernels, and the kernels are merged into clusters. Preferably, the superbinary weights are based on the similarity values according to a probabilistic model. The system of the present invention includes an apparatus for measuring the similarity values, a memory for storing the similarity values, and a proccesor for implementing the method of the present invention.

Abstract: A method and system for applying arbitrary similarity metrics to data entities in order to partition the entities into subsets of related entities. The method and system iteratively construct successive subsets, during construction of each subset adding candidate entities, not yet assigned to a subset, with high affinities toward the subset and removing entities previously assigned to the subset for which the affinities toward the subset have decreased. The method and system efficiently partition data with a high probability of finding an optimal or near-optimal partitioning.