Method for identifying biomarkers using Fractal Genomics Modeling

• US 20050026199A1
• Filed: 07/10/2004
• Published: 02/03/2005
• Est. Priority Date: 01/21/2000
• Status: Abandoned Application

• Current Assignee: Health Discovery Corporation
• Original Assignee: Health Discovery Corporation
• Inventors: Shaw, Sandy C.
• Application Number:
  US10/887,624
• Publication Number:
  US 20050026199A1
• Time in Patent Office: Days
• US Class Current: 435/6
• 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: G06F 16/358 Browsing; Visualisation therefor
• 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 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 40/30 Unsupervised data analysis

Abstract

This present invention relates to methods of manipulation, storage, modeling, visualization and quantification of datasets. One application of the present invention is related to developing FGM models of datasets represented by the various points in a multi-dimensional map. The invention can be adapted to genomic analysis by Fractal Genomics Modeling (FGM) which can be used to identify biomarkers to develop treatments, diagnoses or prognoses of disease by exploiting the map of interactions and causality—pathway conjecture—rendered by this technology.

Claims

1. A method for modeling gene expression of a small group of genes in a genetic network of a subject comprising:

(a) providing a dataset of gene expression values of the small group of genes from the subject;

(b) providing a surface wherein each point on the surface can serve as a domain for an iterative algorithm;

(c) selecting a point on the surface;

(d) generating a comparison string from the selected point using the iterative algorithm;

(e) scoring the comparison string against the gene expression values in the dataset;

(f) determining if the score of the comparison string meets a pre-determined condition or property; and

(g) marking the point if the score meets the pre-determined condition or property to generate a fractal genomics modeling (FGM) model of the target string on the surface.

2. The method of claim 1, further comprising zooming, wherein the steps (c) through (g) are repeated until the score cannot be improved.

3. The method of claim 1, wherein the steps (c) through (g) are repeated for a plurality of datasets from a plurality of small groups of genes to generate a plurality of FGM models on the surface.

4. The method of claim 1, wherein the subject is a subject diagnosed with a disease or a normal subject with respect to the diagnosed disease.

5. The method of claim 1, wherein the subject is a human subject.

6. The method of claim 4, wherein the disease is Down'"'"'s Syndrome.

7. The method of claim 4, wherein the disease is Human Immunodeficient Virus (HIV) infection.

8. The method of claim 4, wherein the disease is cancer.

9. The method of claim 8, wherein the cancer is leukemia.

10. The method of claim 9, wherein the leukemia is acute lymphoblastic leukemia (ALL).

11. The method of claim 9, wherein the leukemia is acute myeloid leukemia (AML).

12. The method of claim 1, wherein the gene expression is measured in a gene-chip comprising a microarray of the genes in the small group of genes.

13. The method of claim 1, wherein in the small group of genes is part of a larger gene pool from the subject.

14. The method of claim 13, wherein the small group of genes is randomly selected from the larger gene pool.

15. The method of claim 13, wherein the larger gene pool has about 7,000 genes or more.

16. The method of claim 13, wherein the larger gene pool has about 12,000 genes or more.

17. The method of claim 13, wherein the larger gene pool consists of the entire genome of the subject.

18. The method of claim 1, wherein the number of genes in the small gene group is from2to 20.

19. The method of claim 1, wherein the number of genes in the small gene group is 5.

20. The method of claim 1, wherein the number of genes in the small gene group is 7.

21. The method of claim 1, wherein the number of genes in the small gene group is 10.

22. The method of claim 1, wherein the number of genes in the small gene group is 14.

23. The method of claim 3, wherein the plurality of datasets are derived from more than one subject.

24. The method of claim 23, wherein the subjects are selected from a group consisting of subjects diagnosed with a disease, normal subjects with respect to the diagnosed disease and a combination thereof.

25. The method of claim 1, wherein the surface is a complex plane.

26. The method of claim 1, wherein the surface is a multi-dimensional surface.

27. The method of claim 1, wherein the surface is in or around a Mandelbrot set.

28. The method of claim 1, wherein the surface is a Julia set.

29. The method of claim 1, wherein the gene expression value is an absolute value or a relative value relative to another small group of genes from the subject.

30. The method of claim 1, wherein the gene expression value is an overall expression value of the small group of genes.

31. The method of claim 1, wherein the scoring of the comparison string is based on its correlation with the gene expression value of the small group of genes.

32. The method of claim 31, wherein the correlation is a Pearson correlation.

33. The method of claim 32, wherein the comparison string is marked to serve as the FGM model for the gene expression value of the small group of genes if the absolute value of the Pearson correlation is greater than 0.95.

34. The method of claim 3 further comprising identifying a biomarker of a phenotype by:

(a) identifying clusters containing FGM models of the small group of genes corresponding to the phenotype;

(b) individually testing each of the small group of genes across all datasets to verify that the pre-determined condition or property between the small groups of genes is markedly different with regard to the phenotype; and

(c) selecting the small group of genes that produces the most marked difference in the pre-determined condition or property as a biomarker for the particular phenotype.

35. The method of claim 34, wherein the FGM model of the small group of genes is used as the biomarker.

36. The method of claim 34, wherein the phenotype is a phenotype of a disease.

37. The biomarker of claim 36 is used to develop treatments, diagnoses, or prognoses of the disease.

38. A diagnostic test comprising the biomarker of claim 34.

39. A method for identifying a biomarker for a phenotype comprising:

(a) providing a plurality of datasets of gene expression values wherein each dataset is from a small group of genes, and the plurality of datasets is from one or more subjects having the phenotype;

(b) providing a surface wherein each point on the surface can be served as a domain for an iterative algorithm;

(c) selecting a point on the surface;

(d) generating a comparison string from the selected point using the iterative algorithm;

(e) scoring the comparison string against the gene expression values in the dataset;

(f) determining if the score of the comparison string meets a pre-determined Pearson correlation value;

(g) marking the point if the score meets the pre-determined Pearson correlation value to generate a FGM model of the target string on the surface;

(h) repeating steps (c) through (g) for a plurality of the datasets to generate FGM models for said plurality of datasets;

(i) identifying clusters containing FGM models of the same small group of genes corresponding to the phenotype;

(j) individually testing each of the small group of genes across all datasets to verify that the Pearson correlation between the small groups of genes is markedly different with regard to the phenotype; and

(k) selecting the small group of genes that produces the most marked difference in the Pearson correlation as a biomarker for the particular phenotype.

40. The method of claim 39, wherein the plurality of datasets is from a combination of one or more subjects having the phenotype and one or more subjects not having the phenotype.

41. The method of claim 39, wherein the FGM model of the small group of genes is used as the biomarker.

42. The method of claim 39, wherein the phenotype is a phenotype of a disease.

43. The biomarker of claim 39 is used to develop treatments, diagnoses, or prognoses of the disease.

44. A diagnostic test comprising the biomarker of claim 39.

45. The method of claim 39, wherein the disease is Down'"'"'s Syndrome.

46. The method of claim 39, wherein the disease is Human Immunodeficient Virus (HIV) infection.

47. The method of claim 39, wherein the disease is cancer.

48. The method of claim 47, wherein the cancer is leukemia.

49. The method of claim 48, wherein the leukemia is acute lymphoblastic leukemia (ALL).

50. The method of claim 48, wherein the leukemia is acute myeloid leukemia (AML).

51. The method of claim 39, wherein the number of genes is in the small group of genes is from 2 to 20.

52. The method of claim 39, wherein the number of genes in the small group of genes is 5.

53. The method of claim 39, wherein the number of genes in the small group of genes is 7.

54. The method of claim 39, wherein the number of genes in the small group of genes is 10.

55. The method of claim 39, wherein the number of genes in the small group of genes is 14.

56. The method of claim 39, wherein the network of genes consists of the entire genome of the subject.

57. The method of claim 39, wherein pre-determined Pearson correlation value is an absolute of the Pearson correlation greater than 0.95.

58. The method of claim 39, wherein the Pearson correlation is markedly different if the absolute value of the Pearson correlation is equal to or less than 0.95.

59. A biomarker for ALL comprising a small gene-group or its FGM model, the small gene-group is selected from a first group of genes, a second group of genes, and both the first group of genes and the second group of genes wherein the first group of genes is GATA2 GATA-binding protein 2, Alcohol dehydrogenase 6 gene, GB DEF=Protein-tyrosine phosphatase mRNA, Globin gene, Pre-mRNA splicing factor SF2, P32 subunit precursor, Major histocompatibility complex enhancer-binding protein, and MSN Moesin;

and the second group of genes is Onconeural ventral antigen-1 (Nova-1) mRNA, Ini1 mRNA, RORA RAR-related orphan receptor A, FUSE biding protein mRNA, Rar protein mRNA, Fetal ALZ-50-reactive clone 1 (FAC1) mRNA, and MB-1 gene.

60. A biomarker for differentiating T-Cell ALL from B-Cell ALL comprising a small gene-group of 7 genes or its FMG model, the small gene-group consists of:

Onconeural ventral antigen-1 (Nova-1) mRNA, Ini1 mRNA, RORA RAR-related orphan receptor A, FUSE biding protein mRNA, Rar protein mRNA, Fetal ALZ-50-reactive clone 1 (FAC1) mRNA, and MB-1 gene.