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

• US 6,920,451 B2
• Filed: 01/19/2001
• Issued: 07/19/2005
• 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): Corrielus, Jean M.
• Assistant Examiner(s): Ly, Anh
• Application Number:
  US09/766,247
• Publication Number:
  US 20010047376A1
• Time in Patent Office: 1,642 Days
• Field of Search: 707/6, 707/104.1, 707/102
• US Class Current: 1/1
• CPC Class Codes: G06F 16/358 Browsing; Visualisation therefor
• CPC Class Codes: Y10S 707/99936 Pattern matching access
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

There is described a method for manipulation, storage, modeling, visualization, and quantification of datasets, which correspond to target strings. A number of target strings are provided. 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. Preferably these points are located in the complex plane, such as in and/or near the Mandelbrot Set or a Julia Set. These comparison strings are also datasets. 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. The coordinates of the point corresponding to the target string or properties of the comparison string may be stored in memory, a database or a table. Mapped or marked points in a region of interest can be explored by examining a subregion with higher resolution. The points are analyzed and/or compared by examining, either visually or mathematically, their relative locations, their absolute locations within the region, and/or metrics other than location.

Claims

1. A method for dataset modeling and pattern analysis for a plurality of target strings, wherein each target string is a dataset of data, the method comprising:

(a) providing a multidimensional map comprising points which can serve as domains for an iterative algorithm to generate datasets of data, and each point in the map can be represented by coordinates of the man;

(b) mathematically generating from a point in the multidimensional map a comparison string comprising a dataset of data, wherein the point can serve as the domain for the interative algorithm to generate a dataset of data;

(c) scoring the comparison string to determine if the comparison string meets a pre-determined condition or property related to the target string;

(d) marking the point in the multidimensional map if the score of the comparison string meets the pre-determined condition or property, wherein the marked point serves as a model for the target string;

(e) repeating the steps (a)-(d) plurality of comparison strings, to generate multiple marked points on the multidimensional map; and

(f) extracting visually or mathematically any patterns formed within each marked point or between the marked points.

2. The method of claim 1, wherein the step of mathematically generating the comparison string comprises using an iterative algorithm.

3. The method of claim 1, wherein the multidimensional map comprises a set of points in a region of a complex plane.

4. The method of claim 1, wherein the multidimensional map further comprises points in or near a Mandelbrot Set, or points both in and near a Mandelbrot Set, or a Julia Set.

5. The method of claim 1, wherein the step of mathematically generating the comparison string further comprises transforming the data in the dataset of the comparison string to have values within a set of interest.

6. The method of claim 1, wherein the step of mathematically generating the comparison string further comprises laying a grid over the points in the multidimensional map.

7. The method of claim 1, wherein the step of mathematically generating the comparison string further comprises restarting the step of mathematically generating the comparison string if the iteration is unbounded.

8. The method of claim 1, wherein the step of mathematically generating the comparison string further comprises generating a comparison string of any length.

9. The method of claim 1, wherein the step of scoring the comparison string comprises evaluating a function having the comparison string and one of the number of target strings as inputs, wherein the evaluation may be repeated for other of the number of the target strings.

10. The method of claim 9, wherein scoring of the comparison string further comprises studying the determining the function'"'"'s minima and maxima.

11. The method of claim 9, wherein only the comparison string is used as relevant input to the scoring function.

12. The method of claim 1, wherein the determined condition or property comprises the comparison string having the highest score, wherein the score is based on some similarity measure to the target string.

13. The method of claim 1, wherein scoring of the comparison string further comprises preliminary testing of properties of the comparison string alone as a criterion to initiate scoring.

14. The method of claim 1, wherein scoring of the comparison string further comprises a test of the comparison string using the target string.

15. The method of claim 14, wherein not all of the data in the comparison string or the target string are used in the test.

16. The method of claim 14, wherein scoring of the comparison string further comprises a one-to-one comparison between corresponding data in the target string and the comparison string.

17. The method of claim 16, wherein the one-to-one comparison is between corresponding sequential or non-sequential data in the target string and the comparison string.

18. The method of claim 1, wherein marking the point in the multidimensional map 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.

19. The method of claim 1, wherein marking the point in the multidimensional map comprises placing a mark on a point in a video display of the multidimensional map, the video display having pixels representing the points of the map, by changing some graphical property of the corresponding pixel of the point.

20. The method of claim 19, wherein the graphical property of the pixel is color.

21. The method of claim 1, further comprising:

examining a plurality of subregions of the multidimensional map with higher resolution.

22. The method of claim 21, wherein the step of examining a subregion further comprises analyzing or comparing points of interest or both analyzing and comparing points of interest by examining visually or mathematically, their relative locations or absolute locations or both their relative and absolute locations within the region.

23. The method of claim 21, wherein the step of examining a subregion further comprises analyzing or comparing points of interest or both analyzing and comparing points of interest by examining, visually or mathematically, metrics other than location.

24. The method of claim 23, wherein the metrics can be represented by graphic properties.

25. The method of claim 24, wherein the graphic property is shading.

26. The method of claim 21, wherein the step of examining a subregion further comprises repeating the examining step for smaller subregions.

27. The method of claim 21, wherein the step of examining a subregion comprises reformatting of the target or comparison string, or both the target and the comparison strings to improve the precision and resolution of the method.

28. The method of claim 27, wherein the step of examining a subregion comprises a reformatting process methodology of Simulated Annealing, Hill Climbing Algorithms, Genetic Algorithms, or Evolutionary Programming Methods.

29. The method of claim 28, wherein the reformatting process is automated.

30. The method of claim 1, wherein the method is for analyzing large datasets.

31. The method of claim 30, wherein the large dataset is DNA sequence data, protein sequence data, gene expression datasets, demographic data, statistical data, or clinical data from patients.

32. The method of claim 1, wherein the method is for analyzing datasets consisting of heterogeneous data,

33. The method of claim 32, wherein the heterogeneous dataset is a combination of gene expression data and clinical data from patients.

34. The method of claim 1, wherein the method is for data compression.

35. The method of claim 1, wherein the steps are automated.

36. The method of claim 1, wherein separate processes of generating the comparison string and scoring the comparison string are processed simultaneously by a plurality of processors.

37. A method for dataset modeling and pattern analysis for a plurality of target strings, wherein each target string is a dataset of data the method comprising:

(a) providing a multidimensional map comprising points which can serve as domains for an iterative algorithm to generate datasets of data and each point can be represented by coordinates on the map;

(b) mathematically generating from a point in the multidimensional map a comparison string comprising a dataset of data, wherein the point can serve as the domain for the iterative algorithm to generate a dataset of data;

(b) scoring the comparison string by evaluating a function having the comparison string and one of the target strings as inputs, wherein the evaluation may be repeated for a number of the other target strings, to determine for each target string if a mark should be placed on the point in the multidimensional map corresponding to the comparison string;

(d) marking the point in the multidimensional map if it is determined that the point should be marked, wherein the marked point serves as a model for the target string;

(e) repeating steps (a)-(d) for a plurality of comparison strings to generate multiple marked points on the multidimensional map; and

(f) extracting visually or mathematically any patterns formed within each marked point or between the marked points.

38. A system for dataset modeling and pattern analysis for a plurality of target strings, wherein each target string is a dataset the system comprising:

(a) a multidimensional map comprising points in the map which can serve as domains for an iterative algorithm to generate datasets and each point can be represented by coordinates on the map;

(b) means for mathematically generating from the points in the multidimensional map comparison strings, each comparison string comprising a dataset and each of the points can serve as a domain for an iterative algorithm to generate a dataset;

(c) means for scoring the comparison string to determine if the comparison string meets a pre-determined condition or property related to the target string;

(d) means for marking the points in the multidimensional map if the score of the comparison string meets the pre-determined condition or property; and

(e) means for extracting visually or mathematically any patterns formed within each marked point or between the marked points.

39. The system of claim 38, wherein the means for mathematically generating the comparison string comprises means for using an iterative algorithm, wherein the comparison string is calculated from a point in any set of points that can serve as the domain of the iterative algorithm.

40. The system of claim 38, wherein the multidimensional map comprises a region of a complex plane.

41. The system of claim 38, wherein the multidimensional map comprises points in or near a Mandelbrot Set, or points both in and near a Mandelbrot Set, or a Julia Set.

42. The system of claim 38, wherein the means for mathematically generating the comparison string further comprises means for transforming the data in the dataset of the comparison string to have values within a set of interest.

43. The system of claim 38 further comprises means for laying a grid over the points in the multidimensional map.

44. The system of claim 38, wherein the means for mathematically generating the comparison string further comprises means for restarting mathematically generating the comparison string if the iteration is unbounded.

45. The system of claim 38, wherein the means for mathematically generating the comparison string further comprises means for generating a comparison string of any length.

46. The system of claim 38, wherein the means for scoring of the comparison string is a means for evaluating a function having the comparison string and one of the number of target strings as inputs, wherein the evaluation may be repeated for other of the number of the target strings.

47. The system of claim 46, wherein the means for scoring the comparison string further comprises means for determining the function'"'"'s minima and maxima.

48. The system of claim 46, wherein only the comparison string is used as relevant input to the scoring function.

49. The system of claim 38, wherein the pre-determined condition or property comprises the comparison string having the highest score, wherein the score is based on some similarity measure to the target string.

50. The system of claim 38, wherein means for scoring of the comparison string further comprises means for preliminary testing of properties of the comparison string alone as a criterion to initiate scoring.

51. The system of claim 38, wherein the means for scoring of the comparison string further comprises a test of the comparison string using the target string.

52. The system of claim 51, wherein not all of the data in the comparison string or the target string are used in the test.

53. The system of claim 51, wherein the means for scoring the comparison string further comprises a one-to-one comparison between the corresponding data in the target string and in the comparison string.

54. The system of claim 53, wherein the one-to-one comparison is between corresponding sequential or non-sequential data in the target string and in the comparison string.

55. The system of claim 38, wherein the means for marking the point in the multidimensional map comprises means for storing the coordinates of the point corresponding to the target string or storing the properties of the comparison string in memory, a database or a table.

56. The system of claim 38, wherein the means for marking the point in the multidimensional map comprises means for placing a mark on a point in a video display of the multidimensional man, the video display having pixels representing the points of the map, by changing some graphical property of the corresponding pixel.

57. The method of claim 56, wherein the graphical property of the pixel is color.

58. The system of claim 38, further comprising:

means for examining a plurality of subregions of the multidimensional map with higher resolution.

59. The system of claim 58, wherein the means for examining a subregion comprises means for reformatting the target or comparison string, or both the target and the comparison strings to improve the precision and resolution of the system.

60. The system of claim 59, wherein the means for examining the subregion comprises a reformatting process methodology of Simulated Annealing, Hill Climbing Algorithms, Genetic Algorithms, or Evolutionary Programming Methods.

61. The system of claim 58, wherein the means for examining a subregion further comprises means for analyzing or comparing points of interest or both analyzing and comparing points of interest by examining visually or mathematically their relative locations or absolute locations or both their relative and absolute locations within the region.

62. The system of claim 58, wherein the means for examining a subregion further comprises means for analyzing or comparing points of interest or both analyzing and comparing points of interest by examining visually or mathematically metrics other than location.

63. The system of claim 62, wherein the metrics can be represented by graphical properties.

64. The method of claim 63, wherein the graphic property is shading.

65. The system of claim 58, wherein the means for examining a subregion further comprises means for repeating the means for examining for smaller subregions.

66. The system of claim 38, wherein the system is used for analyzing large datasets.

67. The method of claim 66, wherein the large dataset is DNA sequence data, protein sequence data, gene expression datasets, demographic data, statistical data, or clinical data from patients.

68. The system of claim 38, wherein the system is used for analyzing datasets consisting of heterogeneous data.

69. The method of claim 68, wherein the heterogeneous dataset is a combination of gene expression data and clinical data from patients.

70. The system of claim 38, wherein the system is used for data compression.

71. The system of claim 38, further comprising a plurality of processors for simultaneously providing the means for generating and for providing the means for scoring the comparison string.