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: 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.

Abstract: Classification of objects using the best boolean expression that represents the most optimal combination of the underlying features is disclosed.

Abstract: Classification of objects using the best boolean expression that represents the most optimal combination of the underlying features is disclosed.

Abstract: A method, information processing system, and computer readable medium, are provided for analyzing a protein folding process. The method includes conducting an incremental pattern discovery process. The incremental pattern discovery process includes judging multidimensional data from a simulation of a protein folding process. The incremental pattern discovery process captures at least one intermediate data point in at least one pattern associated with the protein folding process.

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: Given an input sequence of data, a rigid pattern is a repeating sequence, possibly interspersed with don't-care characters. The data can be a sequence of characters or sets of characters or even real values. In practice, the patterns or motifs of interest are the ones that also allow a variable number of gaps (or don't-care characters): these are patterns with spacers termed extensible patterns. In a bioinformatics context, similar patterns have also been called flexible patterns or motifs. A system according to the invention discovers all the maximal extensible motifs in the input. The flexibility is succinctly defined by a single integer parameter D?1 which is interpreted as the allowable space to be between 1 and D characters between two successive solid characters in a reported motif.

Abstract: Given an input sequence of data, a motif is a repeating pattern. The data could be a sequence of characters or sets of characters or even real values. In the first two cases, the number of motifs could potentially be exponential in the size of the input sequence and in the third case there could be uncountably infinite number of motifs. By suitably defining the notion of maximality and redundancy for any sequence with n characters, there exists only a linear (or no more than 3n) number of special motifs and every other motif can be generated from these irredundant motifs.

Abstract: 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.

Abstract: The method of the present invention aligns a set of N sequences, where N is large. The alignment brings out the best commonality of the N sequences. The method is performed in two stages. A first stage involving discovering motifs, and a second stage involve motif pruning and sequence alignment. The present invention also provides an additional constraint, K, as a user defined control parameter. The additional parameter constrains the alignment of the N sequences to have at least K of the N sequences agree on a character, whenever possible, in the alignment. The alignment number, K, provides a natural constraint for dealing with a large number of sequences in that a commonality across most, if not all sequences is required to be detected.