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: Methods and systems of extracting extensible motifs from a sequence include assigning a significance to extensible motifs within the sequence based upon a syntactic and statistical analysis, and identifying extensible motifs having a significance that exceeds a predetermined threshold.

Abstract: Methods and systems of extracting extensible motifs from a sequence include assigning a significance to extensible motifs within the sequence based upon a syntactic and statistical analysis, and identifying extensible motifs having a significance that exceeds a predetermined threshold.

Abstract: Methods and systems of extracting extensible motifs from a sequence include assigning a significance to extensible motifs within the sequence based upon a syntactic and statistical analysis, and identifying extensible motifs having a significance that exceeds a predetermined threshold.

Abstract: Methods and systems of extracting extensible motifs from a sequence include assigning a significance to extensible motifs within the sequence based upon a syntactic and statistical analysis, and identifying extensible motifs having a significance that exceeds a predetermined threshold.

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: Methods and systems of extracting extensible motifs from a sequence include assigning a significance to extensible motifs within the sequence based upon a syntactic and statistical analysis, and identifying extensible motifs having a significance that exceeds a predetermined threshold.

Abstract: A method (and structure) of data processing in which data is represented in a lossy data format as a plurality of extensible motifs. Each extensible motif has at least one don't-care character enclosed by at least one non-don't-care character on a left side and at least one non-don't-care character on a right side.

Abstract: A method (and structure) of data processing in which data is represented in a lossy data format as a plurality of extensible motifs. Each extensible motif has at least one don't-care character enclosed by at least one non-don't-care character on a left side and at least one non-don't-care character on a right side.