Method, system and software arrangement for reconstructing formal descriptive models of processes from functional/modal data using suitable ontology

• US 7,801,841 B2
• Filed: 06/20/2006
• Issued: 09/21/2010
• Est. Priority Date: 06/20/2005
• Status: Active Grant

• Current Assignee: New York University
• Original Assignee: New York University
• Inventors: Antoniotti, Marco, Mishra, Bhubaneswar, Ramakrishnan, Naren
• Primary Examiner(s): Sparks; Donald
• Assistant Examiner(s): Fernandez Rivas; Omar F
• Application Number:
  US11/471,913
• Publication Number:
  US 20080147382A1
• Time in Patent Office: 1,554 Days
• Field of Search: 706 45- 46, 706 55- 58, 706/60, 706/62, 707 1- 3, 702/1, 702 19- 21
• US Class Current: 706/55
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A method, system and software arrangement in accordance with an exemplary embodiment of the present invention are provided to extract descriptive narrative from numerical experimental data augmented with ontological controlled vocabulary. One exemplary application of such system, method and software arrangement is in organizing gene-expression time course data in terms of biological processes that may be activated and deactivated as the biological system responds to its normal or perturbed environment. The present invention may also have biological applications to drug-or-vaccine discovery, understanding behavior of a cell in an altered diseased state (e.g., cancer, neuro-degeneration or auto-immune disease, etc.), genetically modifying a natural wild-type organism, genetic-engineering, etc. Other exemplary applications may include understanding neural behavior, market behavior of a population of users interacting on the Internet, etc.

Claims

1. A method for at least one of generating or utilizing a model associated with a data set using predetermined semantics, comprising:

organizing the data set into a plurality of states and a plurality of state transitions to generate the model, wherein at least one transition of the plurality of state transitions is associated with each of the states;

associating each of the states with at least one label relating to the predetermined semantics;

assigning at least one probability to at least one state of the plurality of states based on a likelihood that the at least one state follows the at least one transition associated with the at least one state; and

using a computing arrangement, determining at least one invariant associated with the model as a function of the at least one probability, wherein the at least one invariant is determined using at least one of a modal logic technique, a linear-time temporal logic technique, a branching-time temporal logic technique, or a fuzzy logic technique.

2. The method of claim 1, further comprising:

receiving a query relating to the data set; and

providing a response to the query based at least in part on the model.

3. The method of claim 1, further comprising:

re-organizing the data set into a plurality of further states and a plurality of further state transitions based at least in part on the at least one probability assigned to the at least one state, wherein at least one further transition of the plurality of further state transitions is associated with each of the further states.

4. The method of claim 1, wherein the data set comprises at least one of modal data, temporal data or functional data.

5. The method of claim 3, wherein the data set is associated with at least one of a gene expression, a probe value, a click on a web link, or a cellular event.

6. The method of claim 1, wherein the data set is arranged in a form of a matrix.

7. The method of claim 1, wherein the data set is organized using at least one of a clustering procedure, a K-means procedure, an SOM procedure, an agglomerative procedure, a graph-based procedure, a biclustering procedure, or an information-bottleneck-based procedure.

8. The method of claim 1, wherein the predetermined semantics are provided by at least one of a controlled vocabulary, an ontology, a gene ontology, a prior knowledge relating to the data set, a procedure which operates on gene expression data, or a statistical text mining procedure.

9. The method of claim 1, wherein the at least one probability is determined using at least one of a Fisher exact test or a Jacquard coefficient technique.

10. The method of claim 1, wherein the at least one invariant is determined by combining at least two labels.

11. The method of claim 10, further comprising combining at lest two labels using at least one of a model checking technique or an iterative extension technique.

12. The method of claim 1, further comprising at least one of displaying or storing information related to the model in a storage arrangement in at least one of a user-accessible format or a user-readable format.

13. The method of claim 1, wherein at least one of the states or the state transitions comprise at least one of a time factor or a temporal component.

14. A system for at least one of generating or utilizing a model associated with a data set using predetermined semantics, comprising:

a processing arrangement; and

a computer-readable medium which includes thereon a set of instructions, wherein the set of instructions is configured to program the processing arrangement to;

(a) organize the data set into a plurality of states and a plurality of state transitions to generate the model, wherein at least one transition of the plurality of state transitions is associated with each of the states;

(b) associate each of the states with at least one label relating to the predetermined semantics;

(c) assign at least one probability to at least one state of the plurality of states based on a likelihood that the at least one state follows the at least one transition associated with the at least one state; and

(d) determine at least one invariant associated with the model as a function of the at least one probability, wherein the at least one invariant is determined using at least one of a modal logic technique, a linear-time temporal logic technique, a branching-time temporal logic technique, or a fuzzy logic technique.

15. The system of claim 14, wherein the set of instructions is further configured to program the processing arrangement to:

(e) receive a query relating to the data set; and

(f) provide a response to the query based at least in part on the model.

16. The system of claim 14, wherein the set of instructions is further configured to program the processing arrangement to re-organize the data set into a plurality of further states and a plurality of further state transitions based at least in part on the at least one probability assigned to the at least one state, wherein at least one further transition of the plurality of further state transitions is associated with each of the further states.

17. The system of claim 14, wherein the set of instructions is configured to program the processing arrangement to organize the data set using at least one of a clustering procedure, a K-means procedure, an SOM procedure, an agglomerative procedure, a graph-based procedure, a biclustering procedure, or an information-bottleneck-based procedure.

18. The system of claim 14, wherein the set of instructions is further configured program the processing arrangement to at least one of display or store information related to the model in a storage arrangement in at least one of a user-accessible format or a user-readable format.

19. The system of claim 14, wherein at least one of the states or the state transitions comprise at least one of a time factor or a temporal component.

20. A software arrangement, stored on a computer-readable medium, for at least one of generating or utilizing a model associated with a data set using predetermined semantics, comprising:

a first set of instructions which, when executed by a processing arrangement, configure the processing arrangement to organize the data set into a plurality of states and a plurality of state transitions to generate the model, wherein at least one transition of the plurality of state transitions is associated with each of the states;

a second set of instructions which, when executed by the processing arrangement, configure the processing arrangement to associate each of the states with at least one label relating to the predetermined semantics;

a third set of instructions which, when executed by the processing arrangement, configure the processing arrangement to assign at least one probability to at least one state of the plurality of states based on a likelihood that the at least one state follows the at least one transition associated with the at least one state; and

a fourth set of instructions which, when executed by the processing arrangement, configure the processing arrangement to determine at least one invariant associated with the model as a function of the at least one probability, wherein the at least one invariant is determined using at least one of a modal logic technique, a linear-time temporal logic technique, a branching-time temporal logic technique, or a fuzzy logic technique.

21. The software arrangement of claim 20, further comprising:

a fifth set of instructions which, when executed by the processing arrangement, configure the processing arrangement to receive a query relating to the data-set; and

a sixth set of instructions which, when executed by the processing arrangement, configure the processing arrangement to provide a response to the query based at least in part on the model.

22. The software arrangement of claim 20, further comprising:

a further set of instructions which, when executed by the processing arrangement, configure the processing arrangement to re-organize the data set into a plurality of further states and a plurality of further state transitions based at least in part on the at least one probability assigned to the at least one state, wherein at least one further transition of the plurality of further state transitions is associated with each of the further states.

23. The software arrangement of claim 20, wherein the first set of instructions, when executed by the processing arrangement, configure the processing arrangement to organize the data set using at least one of a clustering procedure, a K-means procedure, an SOM procedure, an agglomerative procedure, a graph-based procedure, a biclustering procedure, or an information-bottleneck-based procedure.

24. The software arrangement of claim 20, further comprising a further set of instructions which, when executed by the processing arrangement, configure the processing arrangement to at least one of display or store information related to the model in a storage arrangement in at least one of a user-accessible format or a user-readable format.

25. The software arrangement of claim 20, wherein at least one of the states or the state transitions comprise at least one of a time factor or a temporal component.

26. A non-transitory computer-accessible medium, which has stored thereon computer executable instructions for at least one of generating or utilizing a model associated with a data set using predetermined semantics, which, when executed by a hardware processing arrangement, configure the hardware processing arrangement to execute-procedures-comprising:

(a) organize the data set into a plurality of states and a plurality of state transitions to generate the model, wherein at least one transition of the plurality of state transitions is associated with each of the states;

(b) associate each of the states with at least one label relating to the predetermined semantics;

(c) assign at least one probability to at least one state of the plurality of states based on a likelihood that the at least one state follows the at least one transition associated with the at least one state; and

(d) determine at least one invariant associated with the model as a function of the at least one probability, wherein the at least one invariant is determined using at least one of a modal logic technique, a linear-time temporal logic technique, a branching-time temporal logic technique, or a fuzzy logic technique.

27. The computer-accessible medium of claim 26, wherein the set of instructions is further configured to program the processing arrangement to:

(e) receive a query relating to the data set; and

(f) provide a response to the query based at least in part on the model.

28. The computer-accessible medium of claim 26, wherein the set of instructions is further configured to program the processing arrangement to re-organize the data set into a plurality of further states and a plurality of further state transitions based at least in part on the at least one probability assigned to the at least one state, wherein at least one further transition of the plurality of further state transitions is associated with each of the further states.

29. The computer-accessible medium of claim 26, wherein the set of instructions is configured to program the processing arrangement to organize the data set using at least one of a clustering procedure, a K-means procedure, an SOM procedure, an agglomerative procedure, a graph-based procedure, a biclustering procedure, or an information-bottleneck-based procedure.

30. The computer-accessible medium of claim 26, wherein the at least one invariant is determined by combining at least two labels.

31. The computer-accessible medium of claim 30, further comprising combining at lest two labels using at least one of a model checking technique or an iterative extension technique.

32. The computer-accessible medium of claim 26, wherein the set of instructions is further configured to program the processing arrangement to at least one of display or store information related to the model in a storage arrangement in at least one of a user-accessible format or a user-readable format.

33. The computer-accessible medium of claim 26, wherein at least one of the states or the state transitions comprise at least one of a time factor or a temporal component.