Method for identifying biomarkers using Fractal Genomics Modeling

• US 20050079524A1
• Filed: 08/17/2004
• Published: 04/14/2005
• 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:
  US10/920,035
• Publication Number:
  US 20050079524A1
• 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: 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 identifying biomarkers for a phenotype or determining a genetic pathway of a larger gene pool regarding the phenotype in a population, the method comprising:

a) providing a plurality of subjects from the population, the plurality of subjects having a positive group with the phenotype and a negative group without the phenotype;

b) obtaining gene expression values of the genes in the larger gene pool from each subject;

c) randomly breaking the gene expression values into subgroups for each subject, each subgroup containing N gene expression values from a small group of N genes;

d) developing a single point-model for each subgroup of gene expression values using a fractal genomics modeling (FGM) method generated from a multi-dimensional FGM surface;

e) mapping the FGM point models of each subgroup of genes for all subjects on the FGM surface;

f) clustering the point models based on their proximity on the surface;

g) identifying a small group of genes in a cluster containing the same group of genes for a significant number of either the positive or negative subjects;

h) determining the correlative patterns between the small group of genes for all subjects in the plurality of subjects to determine if the subgroup effectively differentiates the positive from the negative subjects; and

i) identifying the small group of genes in step (h) that positively differentiates the positive from the negative subjects as a candidate for a biomarker for the phenotype in the population.

2. The method of claim 1, further comprising repeating steps (c) through (i) wherein the gene expression values are scrambled to generate a different ordering of the genes in the larger gene pool to find another biomarker or another pathway.

3. The method of claim 1, further comprising evaluating the candidate for biomarker wherein the candidate is listed as a biomarker for the phenotype if it successfully allows classification of all the subjects tested.

4. The method of claim 3, wherein the candidate successfully allows classification of all the subjects tested when:

(a) the candidate only produces valid models for all phenotype positive or all phenotype negative subjects; and

(b) the candidate'"'"'s expression values from a single subject produce a ranked differentiation by phenotype when correlated with the other subjects.

5. The method of claim 3, wherein a weighted procedure is performed to determine which genes in the biomarker are deemed most important in distinguishing phenotype, the procedure comprising:

(a) calculating the average expression value for each of the N genes for the positive and the negative subjects with regard to the phenotype;

(b) evaluating the correlation value between the two N average gene expression values from the phenotype positive subjects and the phenotype negative subjects;

(c) making each of the N genes the average phenotype negative expression value equivalent to the phenotype positive value, calculating the correlation between all N values and evaluating the absolute value of the difference from the correlation calculated in step (b);

(d) ranking the N values generated in step (c) from highest to lowest and giving the weight of 1.0 to the highest value; and

(e) taking the ratios of the remaining values other the highest ranked value to produce the weighting of the other values.

6. The method of claim 5 further comprises checking the biomarkers identified against known pathways and networks.

7. The method of claim 1 wherein the larger gene pool consists of more than about 10,000 genes.

8. The method of claim 1 wherein the larger gene pool consists of the entire genome of the subject in the population.

9. The method of claim 1 wherein gene expression values are obtained by using a gene chip.

10. The method of claim 1 wherein the number of genes N from the small gene group is from 5 to 20.

11. The method of claim 1 wherein the subject is a human being.

12. The method of claim 1 wherein the phenotype is a disease.

13. The method of claim 12, wherein the disease is cancer.

14. The method of claim 12, wherein the disease is human immunodeficiency virus (HIV) infection.

15. The method of claim 12, wherein the disease is Down'"'"'s Syndrome.

16. The method of claim 1, wherein the correlative pattern is a Pearson correlation.

17. The method of claim 1, wherein the significant number in step (g) is equal to two or greater.

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

19. The method of claim 1, wherein the surface is derived from a Julia set.

20. The method of claim 1, wherein the surface is from or near the boundary of a Mandlebrot set.

21. The method of claim 4, wherein the models are valid when the absolute value of Pearson correlation is greater than 0.95.

22. The method of claim 5, wherein the correlation is a Pearson correlation.

23. The method of claim 5, further comprising checking the biomarkers against known pathways and networks to give extra significance to the biomarkers based on biochemical considerations.

24. The method of claim 1 wherein the method is automated.

25. The method of claim 2, wherein the method is automated.

26. The method of claim 4, wherein the method is automated.

27. The method of claim 5, wherein the method is automated.