Method for the manipulation, storage, modeling, visualization and quantification of datasets

• US 7,366,719 B2
• Filed: 10/06/2004
• Issued: 04/29/2008
• Est. Priority Date: 01/21/2000
• Status: Expired due to Fees

• Current Assignee: Health Discovery Corporation
• Original Assignee: Health Discovery Corporation
• Inventors: Shaw, Sandy C.
• Primary Examiner(s): Truong, Cam-Y
• Assistant Examiner(s): Ly, Anh
• Application Number:
  US10/959,844
• Publication Number:
  US 20050076190A1
• Time in Patent Office: 1,301 Days
• Field of Search: 707/6, 707/7, 707100-1041
• US Class Current: 1/1
• CPC Class Codes: C12Q 1/6883 for diseases caused by alterations of genetic material
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: Y10S 707/99931 Database or file accessing
• CPC Class Codes: Y10S 707/99936 Pattern matching access
• CPC Class Codes: Y10S 707/99937 Sorting

Abstract

There is described a method for manipulation, storage, modeling, visualization, and quantification of datasets, which correspond to target strings. An iterative algorithm is used to generate comparison strings corresponding to some set of points that can serve as the domain of an iterative function. The comparison string is scored by evaluating a function having the comparison string and one of the plurality of target strings as inputs. The score measures a relationship between a comparison string and a target string. The evaluation may be repeated for a number of the other target strings. The score or some other property corresponding to the comparison string is used to determine the target string'"'"'s placement on a map. The target string may also be marked by a point on a visual display.

Claims

1. A computerized method for visualizing patterns within a plurality of datasets, the method comprising:

(a) inputting the plurality of datasets into a computer;

(b) defining a first dataset of the plurality of datasets comprising a target string comprising a numerical sequence;

(c) defining a region comprising a plurality of points, wherein each point serves as a domain of an iterative algorithm for generating a comparison string, and wherein the region is selected from the group consisting of a Mandelbrot set and a Julia set;

(d) selecting a point in the region;

(e) generating a comparison string comprising a second dataset from the selected point using the iterative algorithm;

(f) scoring the comparison string to generate a score by testing the comparison string against the target string;

(g) determining if the score meets a pre-determined condition;

(h) marking the point within the region if the score meets the pre-determined condition to represent a point-model of the first dataset in a human readable format; and

(i) generating a visual display wherein the point is graphically displayed at coordinates within a grid that corresponds to the region, and wherein a location of the point within the region is an indicator of similarity of the first dataset to other datasets of the plurality of datasets.

2. The method of claim 1, wherein the steps (d) through (h) are repeated for a plurality of target strings corresponding to one or more other datasets of the plurality to generate a plurality of point-models in the region to form a map of the point-models.

3. The method of claim 1, wherein the step of selecting a point comprises selecting the point within the grid of the visual display.

4. The method of claim 1, wherein the dataset of the comparison string is of any length.

5. The method of claim 1, wherein the step of generating the comparison string further comprises selecting a new point for iteration if the iteration from the point becomes unbounded.

6. The method of claim 1, wherein scoring comprises a one-to-one comparison between the corresponding data in the target string and in the comparison string.

7. The method of claim 6, wherein the one-to-one comparison is between corresponding sequential or non-sequential data in the target string and in the comparison string.

8. The method of claim 1, wherein scoring of the comparison and target strings is based on Pearson Correlation between the strings.

9. The method of claim 1, wherein marking comprises storing the coordinates of the point corresponding to the target string or properties of the comparison string in memory, a database, or a table.

10. The method of claim 1, wherein the point is displayed by changing color of a pixel in the grid.

11. The method of claim 1, wherein the point-model functions as data compression for the target string.