Automated hypothesis testing

• US 20030033127A1
• Filed: 09/10/2002
• Published: 02/13/2003
• Est. Priority Date: 03/13/2001
• Status: Abandoned Application

• Current Assignee: Bioanalytics Holdings Pty., Ltd.
• Original Assignee: Bioanalytics Holdings Pty., Ltd.
• Inventors: Lett, Gregory Scott
• Application Number:
  US10/238,167
• Publication Number:
  US 20030033127A1
• Time in Patent Office: Days
• US Class Current: 703/11
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/10064 Fluorescence image
• CPC Class Codes: G06T 5/002 Denoising; Smoothing noise processing or correction adapted to be used in an image pickup device containing and electronic image sensor H04N23/81, H04N25/60 - H04N25/67
• CPC Class Codes: G06T 5/70 Denoising; Smoothing

Abstract

The present invention relates to a method and system for automatically constructing computer simulation models of biological systems. More specifically, a series of simulation models are created, or selected from a repository of standard models, preferably based on experimental data. These models are then calibrated, if necessary, based upon experimental data and then compared to each other for goodness of fit to a set of experimental data; the best models can then be selected based upon the goodness-of-fit calculations. Another aspect of the invention provides for automated design of additional experiments to differentiate between models that have the same or similar goodness-of-fit scores.

Claims

1. A method for automated hypothesis testing, said method comprising the steps of:

a. generating or selecting a plurality of computer simulation models of a biological or physiological system;

b. calibrating said plurality of models;

c. comparing or ranking said plurality of models based upon the goodness of fit of each model to experimental data; and

d. designing at least one experiment to help differentiate between two or more statistically equivalent models.

2. The method of claim 1 further comprising the step of performing said experiment designed in step d.

3. The method of claim 1 further comprising the step of modifying at least one of the plurality of models generated or selected in step a.

4. The method of claim 1 wherein the step of generating or selecting said plurality of models includes use of an expert system or machine learning algorithm.

5. The method of claim 1 wherein said calibration step is based at least in part on information about the experimental protocols used to generate the experimental data used in the calibration step or any earlier steps.

6. The method of claim 1 wherein said calibration step uses a batch estimator or recursive filter.

7. The method of claim 6 wherein said batch estimator is selected from the group consisting of the Levenberg-Marquardt method, the Nelder-Mead method, the steepest descent method, Newton'"'"'s method, and the inverse Hessian method.

8. The method of claim 6 wherein said recursive filter is selected from the group consisting of the least-squares filter, the pseudo-inverse filter, the square-root filter, the Kalman filter and Jazwinski'"'"'s adaptive filter.

9. The method of claim 1 wherein said calibration step uses a neural network algorithm, a hybrid neural network algorithm or self-organizing map.

10. The method of claim 1 wherein said comparison or ranking step uses a subset of data not used in said calibration step.

11. The method of claim 1 wherein said comparison or ranking step includes a penalty for model complexity.

12. The method of claim 1 wherein said comparison or ranking step uses the Chi-square test, the Kolmogorov-Smirnoff test or the Anderson-Darling test.

13. The method of claim 1 wherein said comparison or ranking step uses the Akaike Information Criterion.

14. The method of claim 1 wherein said comparison or ranking step uses a non-parametric statistical test.

15. A system for automated hypothesis testing, said system comprising the steps of:

a. a hypothesis generation system for generating or selecting a plurality of computer simulation models of a biological or physiological system;

b. a parameter estimation system for calibrating said plurality of models;

c. a model scoring system for comparing or ranking said plurality of models based upon the goodness of fit of each model to experimental data; and

d. an experimental design system for designing at least one experiment to help differentiate between two or more statistically equivalent models.

16. The system of claim 15 further comprising an experimental data gathering system for performing the experiment designed by said experimental design system.

17. The system of claim 15 wherein said hypothesis generation system modifies at least one of said plurality of models generated or selected by said hypothesis generation system.

18. The system of claim 15 wherein said hypothesis generation system uses an expert system or machine learning algorithm.

19. The system of claim 15 wherein said parameter estimation system utilizes information about the experimental protocols used to generate the experimental data used in the calibration step or any earlier steps.

20. The method of claim 15 wherein said parameter estimation system uses a batch estimator or recursive filter.

21. The method of claim 20 wherein said batch estimator is selected from the group consisting of the Levenberg-Marquardt method, the Nelder-Mead method, the steepest descent method, Newton'"'"'s method, and the inverse Hessian method.

22. The method of claim 20 wherein said recursive filter is selected from the group consisting of the least-squares filter, the pseudo-inverse filter, the square-root filter, the Kalman filter and Jazwinski'"'"'s adaptive filter.

23. The method of claim 15 wherein said parameter estimation system uses a neural network algorithm, a hybrid neural network algorithm or self-organizing map.

24. The method of claim 15 wherein said model scoring system uses a subset of data not used in said calibration step.

25. The method of claim 15 wherein said model scoring system includes a penalty for model complexity.

26. The method of claim 15 wherein said model scoring system uses the Chi-square test, the Kolmogorov-Smirnoff test or the Anderson-Darling test.

27. The method of claim 15 wherein said model scoring system uses the Akaike Information Criterion.

28. The method of claim 15 wherein said model scoring system uses a non-parametric statistical test.

29. A method for automated hypothesis testing, said method comprising the steps of:

a. generating or selecting a plurality of computer simulation models of a biological or physiological system;

b. calibrating said plurality of models; and

c. comparing or ranking said plurality of models based upon the goodness of fit of each model to experimental data.

30. The method of claim 29 further comprising the step of modifying at least one of the plurality of models generated or selected in step a.

31. The method of claim 29 wherein the step of generating or selecting said plurality of models includes use of an expert system or machine learning algorithm.

32. The method of claim 29 wherein said calibration step is based at least in part on information about the experimental protocols used to generate the experimental data used in the calibration step or any earlier steps.

33. The method of claim 29 wherein said calibration step uses a batch estimator or recursive filter.

34. The method of claim 33 wherein said batch estimator is selected from the group consisting of the Levenberg-Marquardt method, the Nelder-Mead method, the steepest descent method, Newton'"'"'s method, and the inverse Hessian method.

35. The method of claim 33 wherein said recursive filter is selected from the group consisting of the least-squares filter, the pseudo-inverse filter, the square-root filter, the Kalman filter and Jazwinski'"'"'s adaptive filter.

36. The method of claim 29 wherein said calibration step uses a neural network algorithm, a hybrid neural network algorithm or self-organizing map.

37. The method of claim 29 wherein said comparison or ranking step uses a subset of data not used in said calibration step.

38. The method of claim 29 wherein said comparison or ranking step includes a penalty for model complexity.

39. The method of claim 29 wherein said comparison or ranking step uses the Chi-square test, the Kolmogorov-Smirnoff test or the Anderson-Darling test.

40. The method of claim 29 wherein said comparison or ranking step uses the Akaike Information Criterion.

41. The method of claim 29 wherein said comparison or ranking step uses a non-parametric statistical test.

42. A system for automated hypothesis testing, said system comprising the steps of:

a. a hypothesis generation system for generating or selecting a plurality of computer simulation models of a biological or physiological system;

b. a parameter estimation system for calibrating said plurality of models; and

c. a model scoring system for comparing or ranking said plurality of models based upon the goodness of fit of each model to experimental data.

43. The system of claim 42 wherein said hypothesis generation system modifies at least one of said plurality of models generated or selected by said hypothesis generation system.

44. The system of claim 42 wherein said hypothesis generation system uses an expert system or machine learning algorithm.

45. The system of claim 42 wherein said parameter estimation system utilizes information about the experimental protocols used to generate the experimental data used in the calibration step or any earlier steps.

46. The method of claim 42 wherein said parameter estimation system uses a batch estimator or recursive filter.

47. The method of claim 46 wherein said batch estimator is selected from the group consisting of the Levenberg-Marquardt method, the Nelder-Mead method, the steepest descent method, Newton'"'"'s method, and the inverse Hessian method.

48. The method of claim 46 wherein said recursive filter is selected from the group consisting of the least-squares filter, the pseudo-inverse filter, the square-root filter, the Kalman filter and Jazwinski'"'"'s adaptive filter.

49. The method of claim 42 wherein said parameter estimation system uses a neural network algorithm, a hybrid neural network algorithm or self-organizing map.

50. The method of claim 42 wherein said model scoring system uses a subset of data not used in said calibration step.

51. The method of claim 42 wherein said model scoring system includes a penalty for model complexity.

52. The method of claim 42 wherein said model scoring system uses the Chi-square test, the Kolmogorov-Smirnoff test or the Anderson-Darling test.

53. The method of claim 42 wherein said model scoring system uses the Akaike Information Criterion.

54. The method of claim 42 wherein said model scoring system uses a non-parametric statistical test.