Knowledge-based storage of diagnostic models

• US 20050071143A1
• Filed: 06/04/2004
• Published: 03/31/2005
• Est. Priority Date: 09/29/2003
• Status: Active Grant

• Current Assignee: Cancer Genetics
• Original Assignee: Pathwork Informatics, Inc. (Response Genetics, Inc)
• Inventors: Tran, Quang, Pattin, Alejandrina, Hornbaker, Cheryl, Anderson, Glenda G.
• Granted Patent: US 8,321,137 B2
• Time in Patent Office: Days
• US Class Current: 703/11
• 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 25/30 Microarray design
• 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 40/20 Supervised data analysis
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 70/60 relating to pathologies
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Methods, computer systems, and computer readable medium for testing a plurality of models in order to classify a biological specimen. A determination is made as to whether a model precondition associated with a model in the plurality of models has been satisfied. The model is skipped when the model precondition has not been satisfied. When the model precondition has been satisfied, the first model is tested by selecting a calculation from the first model. The selected calculation is computed using cellular constituent abundance values from the biological specimen in accordance with a calculation algorithm set forth in the selected model. This selecting and computing is repeated for one or more calculations in the selected model. Then, each of these calculations is aggregated in accordance with an aggregation algorithm associated with the selected model. These steps are repeated for at least one other model in the plurality of models. A computer medium comprising a classifier for classifying a biological specimen into a biological sample class that includes one or more models. Each of the one or more models comprises (i) an optional model precondition, (ii) a calculation algorithm, (iii) a plurality of calculations, and (iv) a calculation aggregation algorithm.

Claims

1. A method for classifying a biological specimen, the method comprising:

(A) determining whether a model precondition included within a selected model in a plurality of models for classifying a biological specimen into a biological sample class has been satisfied;

each model in said plurality of models comprising a plurality of calculations, a calculation algorithm, and an aggregation algorithm, and optionally comprising one or more preconditions;

wherein each calculation in each said plurality of calculations specifies an identity of one or more cellular constituents in said biological specimen to be used in a calculation algorithm, and

when the model precondition for the selected model has not been satisfied, the selected model is not used; and

when the model precondition for the selected model has been satisfied, the determining further comprises testing the selected model by;

(i) choosing a calculation from the plurality of calculations in the selected model;

(ii) obtaining a cellular constituent abundance value for each of said one or more cellular constituents specified by said calculation from said biological specimen;

(iii) computing the calculation using the cellular constituent abundance values obtained from the biological specimen in accordance with the calculation algorithm of the selected model;

(iv) repeating steps (i) through (iii) for one or more other calculations in the selected model; and

(v) aggregating each calculation computed in step (iii) for the selected model in accordance with the aggregation algorithm in the selected model; and

(B) repeating step (A) with at least one other model in the plurality of models.

2. The method of claim 1 wherein a calculation algorithm in a model in the plurality of models specifies a ratio between a numerator and a denominator wherein

the numerator is determined by an abundance of a first cellular constituent from the biological specimen;

and

the denominator is determined by an abundance of a second cellular constituent from the biological specimen.

3. The method of claim 2 wherein the first cellular constituent and the second cellular constituent are each a nucleic acid or a ribonucleic acid and the abundance of the first cellular constituent and the abundance of the second cellular constituent in the biological specimen are obtained by measuring a transcriptional state of all or a portion of the first cellular constituent and the second cellular constituent.

4. The method of claim 2 wherein the first cellular constituent and the second cellular constituent are each independently all or a fragment of an mRNA, a cRNA or a cDNA.

5. The method of claim 2 wherein the first cellular constituent and the second cellular constituent are each proteins and the abundance of the first cellular constituent and the abundance of the second cellular constituent are obtained by measuring a translational state of all or a portion of the first cellular constituent and the second cellular constituent.

6. The method of claim 2 wherein the abundance of the first cellular constituent and the abundance of the second cellular constituent are determined by measuring an activity or a post-translational modification of the first cellular constituent and the second cellular constituent.

7. The method of claim 2 wherein the selected model is associated with a biological sample class and the first cellular constituent is up-regulated and the second cellular constituent is down-regulated in the biological sample class relative to another biological sample class.

8. The method of claim 2 wherein the selected model is associated with a biological sample class and the first cellular constituent is down-regulated and the second cellular constituent is up-regulated in the biological sample class relative to another biological sample class.

9. The method of claim 1 wherein the selected model comprises between two and one thousand calculations.

10. The method of claim 1 wherein the selected model comprises between three and one hundred calculations.

11. The method of claim 1 wherein the selected model is associated with a biological sample class and a calculation in the selected model comprises a lower calculation threshold and an upper calculation threshold.

12. The method of claim 11, wherein the calculation algorithm for said calculation comprises a model ratio and

the numerator of the model ratio is determined by an abundance of a first cellular constituent from the biological specimen and wherein an identity of said first cellular constituent is specified in said calculation;

the denominator of the model ratio is determined by an abundance of a second cellular constituent from the biological specimen and wherein an identity of said second cellular constituent is specified in said calculation;

the lower calculation threshold is an upper threshold percentile in a distribution of a first plurality of computed test ratios;

wherein

the numerator and denominator of each test ratio in the first plurality of computed test ratios is respectively determined by an abundance of the first cellular constituent and the second cellular constituent from any biological specimen that is not a member of the biological sample class associated with the selected model;

the upper calculation threshold is a lower threshold percentile in a distribution of a second plurality of computed test ratios;

wherein

the numerator and denominator of each test ratio in the second plurality of computed test ratios is respectively determined by an abundance of the first cellular constituent and the second cellular constituent from any biological specimen that is a member of the biological sample class associated with the selected model.

13. The method of claim 12 wherein said calculation (iii) of step (A) comprises:

identifying the calculation as negative when a value obtained by computing the model ratio is below the lower calculation threshold for the calculation;

identifying the calculation as positive when a value obtained by computing the model ratio is above the upper calculation threshold for the calculation; and

identifying the calculation as indeterminate when a value obtained by computing the model ratio is equal to the lower calculation threshold for the calculation, between the lower calculation threshold and the upper calculation threshold for the calculation, or equal to the upper calculation threshold for the calculation.

14. The method of claim 13 wherein said aggregating comprises:

identifying the selected model as positive when more calculations in the plurality of calculations in the selected model are positive than are negative;

identifying the selected model as negative when more calculations in the plurality of calculations in the selected model are negative than are positive; and

identifying the selected model as indeterminate when the number of positive calculations in the plurality of calculations in the selected model equals the number of negative calculations in the plurality of calculations or all the calculations in the selected model are indeterminate.

15. The method of claim 14 wherein the model precondition comprises a requirement that another model in said plurality of models be identified as negative, positive, or indeterminate prior to testing the selected model.

16. The method of claim 1 wherein the model precondition comprises a requirement that another model in said plurality of models be identified as negative, positive, or indeterminate prior to testing the first model.

17. A computer readable medium having computer-executable instructions for performing the steps of the method of claim 1.

18. A computer system for classifying a biological specimen, the computer system comprising:

a central processing unit;

a memory, coupled to the central processing unit, the memory storing a model testing application comprising;

(A) instructions for determining whether a model precondition included within a selected model in a plurality of models for classifying a biological specimen into a biological sample class has been satisfied;

each model in said plurality of models comprising a plurality of calculations, a calculation algorithm, and an aggregation algorithm, and optionally comprising one or more preconditions;

wherein each calculation in each said plurality of calculations specifies an identity of one or more cellular constituents in said biological specimen to be used in a calculation algorithm;

wherein

when the model precondition for the selected model has not been satisfied, the selected model is not used; and

when the model precondition for the selected model has been satisfied, the instructions for determining further comprise instructions for testing the selected model by;

(i) choosing a calculation from the plurality of calculations in the selected model;

(ii) obtaining a cellular constituent abundance value for each of said one or more cellular constituents specified by said calculation from said biological specimen;

(iii) computing the calculation using the cellular constituent abundance values obtained from the biological specimen in accordance with the calculation algorithm of the selected model;

(iv) repeating steps (i) through (iii) for one or more other calculations in the selected model; and

(v) aggregating each calculation computed in step (iii) for the selected model in accordance with the aggregation algorithm in the selected model; and

(B) instructions for repeating the instructions for determining (A) with at least one other model in the plurality of models.

19. The computer system of claim 18 wherein a calculation algorithm in a model in the plurality of models specifies a ratio between a numerator and a denominator wherein

the numerator is determined by an abundance of a first cellular constituent from the biological specimen;

and

the denominator is determined by an abundance of a second cellular constituent from the biological specimen.

20. The computer system of claim 19 wherein the first cellular constituent and the second cellular constituent are each a nucleic acid or a ribonucleic acid and the abundance of the first cellular constituent and the abundance of the second cellular constituent in the biological specimen are obtained by measuring a transcriptional state of all or a portion of the first cellular constituent and the second cellular constituent.

21. The computer system of claim 19 wherein the first cellular constituent and the second cellular constituent are each independently all or a fragment of an mRNA, a cRNA or a cDNA.

22. The computer system of claim 19 wherein the first cellular constituent and the second cellular constituent are each proteins and the abundance of the first cellular constituent and the abundance of the second cellular constituent are obtained by measuring a translational state of all or a portion of the first cellular constituent and the second cellular constituent.

23. The computer system of claim 19 wherein the abundance of the first cellular constituent and the abundance of the second cellular constituent are determined by measuring an activity or a post-translational modification of the first cellular constituent and the second cellular constituent.

24. The computer system of claim 19 wherein the selected model is associated with a biological sample class and the first cellular constituent is up-regulated and the second cellular constituent is down-regulated in the biological sample class relative to another biological sample class.

25. The computer system of claim 19 wherein the selected model is associated with a biological sample class and the first cellular constituent is down-regulated and the second cellular constituent is up-regulated in the biological sample class relative to another biological sample class.

26. The computer system of claim 18 wherein the selected model comprises between two and one thousand calculations.

27. The computer system of claim 18 wherein the selected model comprises between three and one hundred calculations.

28. The computer system of claim 18 wherein the selected model is associated with a biological sample class and a calculation in the selected model comprises a lower calculation threshold and an upper calculation threshold.

29. The computer system of claim 28, wherein the calculation algorithm for said calculation comprises a model ratio and

the numerator of the model ratio is determined by an abundance of a first cellular constituent from the biological specimen and wherein an identity of said first cellular constituent is specified in said calculation;

the denominator of the model ratio is determined by an abundance of a second cellular constituent from the biological specimen and wherein an identity of said second cellular constituent is specified in said calculation;

the lower calculation threshold is an upper threshold percentile in a distribution of a first plurality of computed test ratios;

wherein

the numerator and denominator of each test ratio in the first plurality of computed test ratios are respectively determined by an abundance of the first cellular constituent and the second cellular constituent from any biological specimen that is not a member of the biological sample class associated with the selected model;

the upper calculation threshold is a lower threshold percentile in a distribution of a second plurality of test ratios;

wherein

the numerator and denominator of each test ratio in the second plurality of computed test ratios are respectively determined by an abundance of the first cellular constituent and the second cellular constituent from any biological specimen that is a member of the biological sample class associated with the selected model.

30. The computer system of claim 29 wherein said computing step (iii) of said instructions for determining (A) comprises:

identifying the calculation as negative when a value obtained by computing the model ratio is below the lower calculation threshold for the calculation;

identifying the calculation as positive when a value obtained by computing the model ratio is above the upper calculation threshold for the calculation; and

identifying the calculation as indeterminate when a value obtained by computing the model ratio is equal to the lower calculation threshold for the calculation, between the lower calculation threshold and the upper calculation threshold for the calculation, or equal to the upper calculation threshold for the calculation.

31. The computer system of claim 30 wherein said aggregating comprises:

identifying the selected model as positive when more calculations in the plurality of calculations in the selected model are positive than are negative;

identifying the selected model as negative when more calculations in the plurality of calculations in the selected model are negative than are positive; and

identifying the selected model as indeterminate when the number of positive calculations in the plurality of calculations in the selected model equals the number of negative calculations in the plurality of calculations or all the calculations in the selected model are indeterminate.

32. The computer system of claim 31 wherein the model precondition comprises a requirement that another model in said plurality of models be identified as negative, positive, or indeterminate prior to testing the selected model.

33. The computer system of claim 18 wherein the model precondition comprises a requirement that another model in said plurality of models be identified as negative, positive, or indeterminate prior to testing the first model.

34. A computer program product for use in conjunction with a computer system, the computer program product comprising a computer readable storage medium and a computer program mechanism embedded therein, the computer program mechanism for classifying a biological specimen into a biological sample class, the computer program mechanism comprising one or more models, each model in said one or more models comprising:

(i) a calculation algorithm;

(ii) a plurality of calculations; and

(iii) a calculation aggregation algorithm.

35. The computer program product of claim 34, where a model in said one or models further comprises:

(iv) one or more model preconditions.

36. The computer program product of claim 34 wherein the calculation algorithm is selected from a plurality of calculation algorithms.

37. The computer program product of claim 35 wherein a model precondition in said one or more model preconditions specifies a condition that must be satisfied at a time prior to the use of said model.

38. The computer program product of claim 37 wherein said condition comprises an execution of a different model in said one or more models.

39. The computer program product of claim 34 wherein a calculation algorithm in a model in the one or more models comprises a ratio between a numerator and a denominator wherein

the numerator is determined by an abundance of a first cellular constituent from the biological specimen;

and

the denominator is determined by an abundance of a second cellular constituent from the biological specimen.

40. The computer program product of claim 39 wherein the first cellular constituent and the second cellular constituent are each a nucleic acid or a ribonucleic acid and the abundance of the first cellular constituent and the abundance of the second cellular constituent in the biological specimen are obtained by measuring a transcriptional state of all or a portion of the first cellular constituent and the second cellular constituent.

41. The computer program product of claim 40 wherein the first cellular constituent and the second cellular constituent are each independently all or a fragment of an mRNA, a cRNA or a cDNA.

42. The computer program product of claim 39 wherein the first cellular constituent and the second cellular constituent are each proteins and the abundance of the first cellular constituent and the abundance of the second cellular constituent are obtained by measuring a translational state of all or a portion of the first cellular constituent and the second cellular constituent.

43. The computer program product of claim 42 wherein the abundance of the first cellular constituent and the abundance of the second cellular constituent are determined by measuring an activity or a post-translational modification of the first cellular constituent and the second cellular constituent.

44. The computer program product of claim 39 wherein the model is associated with a biological sample class and the first cellular constituent is up-regulated and the second cellular constituent is down-regulated in the biological sample class relative to another biological sample class.

45. The computer program product of claim 44 wherein

the first cellular constituent is up-regulated in the biological sample class when the abundance of the first cellular constituent in biological specimens of the biological sample class is greater than the abundance of at least seventy percent of the cellular constituents in biological specimens of the biological sample class for which a plurality of cellular constituent abundance measurements have been made;

and

the second cellular constituent is down-regulated in the biological sample class when the abundance of the second cellular constituent in biological specimens of the biological sample class is less than the abundance of at least thirty percent of the cellular constituents in biological specimens of the biological sample class for which a plurality of cellular constituent abundance measurements have been made.

46. The computer program product of claim 39 wherein the first cellular constituent is down-regulated and the second cellular constituent is up-regulated in the biological sample class relative to another biological sample class.

47. The computer program product of claim 46 wherein

the first cellular constituent is down-regulated in the biological sample class when the abundance of the first cellular constituent in biological specimens of the biological sample class is less than the abundance of at least thirty percent of the cellular constituents in biological specimens of the biological sample class for which a plurality of cellular constituent abundance measurements have been made;

and

the second cellular constituent is up-regulated in the biological sample class when the abundance of the second cellular constituent in biological specimens of the biological sample class is greater than the abundance of at least seventy percent of the cellular constituents in biological specimens of the biological sample class for which a plurality of cellular constituent abundance measurements have been made.

48. The computer program product of claim 34 wherein the plurality of calculations in a model in the one or more models comprises between two and one thousand calculations.

49. The computer program product of claim 34 wherein the plurality of calculations in a model the one or more models comprises between three and one hundred calculations.

50. The computer program product of claim 34 wherein a calculation in the plurality of calculations of a selected model in the one or more models comprises a lower calculation threshold and an upper calculation threshold.

51. The computer program product of claim 50, wherein a calculation algorithm in the selected model is a model ratio and

the numerator of the model ratio is determined by an abundance of a first cellular constituent from the biological specimen that is specified by a first calculation in the selected model;

the denominator of the model ratio is determined by an abundance of a second cellular constituent from the biological specimen that is specified by said first calculation;

the lower calculation threshold is an upper threshold percentile in a distribution of a first plurality of computed test ratios;

wherein

the numerator and denominator of each test ratio in the first plurality of computed test ratios is respectively determined by an abundance of the first cellular constituent the second cellular constituent from any biological specimen that is not a member of the biological sample class associated with the selected model; and

the upper calculation threshold is a lower threshold percentile in a distribution of a second plurality of computed test ratios;

wherein

the numerator and denominator of each test ratio in the second plurality of computed test ratios is respectively determined by an abundance of the first cellular constituent and the second cellular constituent from any biological specimen that is a member of the biological sample class associated with the selected model.

52. The computer program product of claim 51 wherein:

the calculation is characterized as negative when the computed model ratio is below the lower calculation threshold for the calculation;

said calculation is characterized as positive when a value of the computed model ratio is above the upper calculation threshold for the calculation; and

the calculation is characterized as indeterminate when a value of the computed model ratio is equal to the lower calculation threshold for the calculation, between the lower calculation threshold and the upper calculation threshold for the calculation, or equal to the upper calculation threshold for the calculation.

53. The computer program product claim 52 wherein said calculation aggregation algorithm comprises instructions for:

identifying the selected model as positive when more calculations in the plurality of calculations are characterized as positive than are characterized as negative;

identifying the selected model as negative when more calculations in the plurality of calculations are characterized as negative than are characterized as positive; and

identifying the selected model as indeterminate when the number of positive calculations in the plurality of calculations equals the number of negative calculations in the plurality of calculations or all the calculations are indeterminate.