Methods for efficiently mining broad data sets for biological markers

• US 20060259246A1
• Filed: 01/05/2006
• Published: 11/16/2006
• Est. Priority Date: 11/28/2000
• Status: Abandoned Application

• Current Assignee: PPD Biomarker Discovery Sciences LLC (CellCarta Biosciences, Inc.)
• Original Assignee: PPD Biomarker Discovery Sciences LLC (CellCarta Biosciences, Inc.)
• Inventors: Huyn, Nam Q.
• Application Number:
  US11/327,169
• Publication Number:
  US 20060259246A1
• Time in Patent Office: Days
• US Class Current: 702/19
• 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

A biological marker identification method identifies biological markers within broad sets of biological data containing many more measurements than observations. For example, the data can contain thousands of measurements on each blood sample obtained from fewer than 100 subjects, each of which falls into one of a set of clinical classes or is associated with a value of a continuous clinical response variable. At least one biomarker, containing a small subset of measurements, is found that is capable of predicting a clinical endpoint. The biomarker can be used for, e.g., diagnosing disease or assessing response to a drug. First, the set of measurements is reduced to a smaller set of candidate measurements by eliminating measurements that either cannot distinguish among classes or are redundant. Biomarker subsets are then selected from the remaining set of measurements, either by an exhaustive search or a heuristic method that finds good but not necessary globally optimal biomarkers.

Claims

1. A method for identifying biological markers in a set of n biological measurements for each of p observations, wherein n>

p and each observation is associated with a clinical endpoint, each biological marker comprising at most k measurements, wherein k<

p, said method comprising;

a) reducing said set of n measurements to a set of m candidate measurements, wherein said reducing comprises performing a differential significance analysis; and

b) selecting at least two biological markers from said set of m candidate measurements, wherein values of each biological marker predict said clinical endpoints.

12. The method of claim 1, wherein said differential significance analysis is performed in part in dependence on a user-selected significance threshold.

19. The method of claim 1, wherein step (b) comprises simulated annealing.

23. The method of claim 1, further comprising performing a market-basket analysis of said selected biological markers.

2-11. -11. (canceled)

13-18. -18. (canceled)

20-22. -22. (canceled)

24. A method for identifying a biological marker in a set of n biological measurements for each of p observations, wherein n>

p and each observation is associated with a clinical endpoint, each biological marker comprising at most k measurements, wherein k<

p, said method comprising;

a) reducing said set of n measurements to a set of m candidate measurements; and

b) using simulated annealing, selecting a biological marker from said set of m candidate measurements, wherein values of said biological marker predict said clinical endpoints.

25. The method of claim 24, wherein n>

10 p.

26. The method of claim 24, wherein k<

p/5.

27. The method of claim 24, wherein step (a) comprises performing a correlation analysis.

28. The method of claim 27, wherein said correlation analysis comprises a correlation-based cluster analysis.

29. The method of claim 28, wherein said correlation-based cluster analysis comprises a correlation-based hierarchical cluster analysis.

30. The method of claim 27, wherein said correlation analysis is performed in part in dependence on a user-selected correlation threshold.

31. The method of claim 27, wherein said correlation analysis is performed in part in dependence on a user-selected value of m.

32. The method of claim 24, wherein step (a) comprises performing a differential significance analysis.

33. The method of claim 32, wherein said differential significance analysis is performed in part in dependence on a user-selected significance threshold.

34. The method of claim 24, wherein said n measurements have different sources.

35. The method of claim 24, wherein k is a user-selected value.

36. The method of claim 24, wherein k is selected in dependence on a desired computation time.

37. The method of claim 24, wherein m is selected in dependence on a desired computation time.

38. The method of claim 24, further comprising performing a market-basket analysis on said selected biological markers.

39-40. -40. (canceled)

41. A program storage device accessible by a processor, tangibly embodying a program of instructions executable by said processor to perform method steps for a biological marker identification method, wherein said method identifies a biological marker in a set of n biological measurements for each of p observations, wherein n>

p and each observation is associated with a clinical endpoint, each biological marker comprising at most k measurements, wherein k<

p, said method steps comprising;

a) reducing said set of n measurements to a set of m candidate measurements; and

b) using simulated annealing, selecting a biological marker from said set of m candidate measurements, wherein values of said biological marker predict said clinical endpoints.

42. (canceled)