PATHWAY RECOGNITION ALGORITHM USING DATA INTEGRATION ON GENOMIC MODELS (PARADIGM)

• US 20150142465A1
• Filed: 12/19/2014
• Published: 05/21/2015
• Est. Priority Date: 04/29/2010
• Status: Abandoned Application

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Vaske, Charles J., Benz, Stephen C., Stuart, Joshua M., Haussler, David
• Application Number:
  US14/577,522
• Publication Number:
  US 20150142465A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06F 16/2219 Large Object storage; Management thereof
• 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 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/20 Probabilistic models
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• 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

A patient sample specific dynamic pathway map is constructed on the basis of measured patient data and a probabilistic pathway model that is based on attributes for pathway elements, wherein some attributes for pathway elements are known a priori, where other attributes for the pathway elements are assumed, and where the pathway elements are cross-correlated and assigned an influence level for at least one pathway. Preferred dynamic pathway maps provide context of the measured patient data with respect to a selected reference pathway activity.

Claims

1. A method of analyzing biologically relevant information, comprising:

providing access to a pathway database storing a plurality of pathways, wherein at least one of the pathways has an a priori known association with a neoplastic disease;

wherein each of the plurality of pathways comprises a plurality of respective a priori known pathway elements, and wherein at least some of the a priori known pathway elements have an assumed or previously determined attribute;

providing access to a modification engine coupled to the pathway element database;

associating, via the modification engine, at least some of the pathway elements with at least one measured attribute, respectively, wherein the measured attribute is obtained from a patient sample;

determining, via the modification engine, statistically a deregulation score for the at least some of the pathway elements based on the respective measured attributes and assumed or previously determined attributes; and

updating, via the modification engine, a patient sample record using the deregulation score for the at least some of the pathway elements.

2. The method of claim 1 wherein the pathway is within a regulatory pathway network.

3. The method of claim 2 wherein the regulatory pathway network is selected from the group consisting of an ageing pathway network, an apoptosis pathway network, a homeostasis pathway network, a metabolic pathway network, a replication pathway network, and an immune response pathway network.

4. The method of claim 1 wherein the neoplastic disease is a cancer of at least one of the following:

a liver, a breast, a lung, a prostate, a cervix, a colon, a pancreas, a brain and skin.

5. The method of claim 1 wherein the neoplastic disease is a hyperplasia of the prostate or the thyroid.

6. The method of claim 1 wherein the assumed or previously determined attribute of at least some of the a priori known pathway elements is reflective of normal tissue.

7. The method of claim 1 wherein the assumed or previously determined attribute of at least some of the a priori known pathway elements is reflective of diseased tissue.

8. The method of claim 1 wherein the assumed or previously determined attribute is selected from the group consisting of a compound attribute, a class attribute, a gene copy number, a transcription level, a translation level, and a protein activity.

9. The method of claim 8 wherein the assumed or previously determined attribute is a transcription level.

10. The method of claim 8 wherein the assumed or previously determined attribute is a translation level or protein activity.

11. The method of claim 1 wherein the measured attribute is selected from the group consisting of a compound attribute, a class attribute, a gene copy number, a transcription level, a translation level, and a protein activity.

12. The method of claim 11 wherein the measured attribute is a transcription level.

13. The method of claim 11 wherein the measured attribute is a translation level or protein activity.

14. The method of claim 11 wherein the patient sample comprises a diseased tissue.

15. The method of claim 11 wherein the diseased tissue comprises a neoplastic tissue.

16. The method of claim 1 wherein statistical determination comprises cross-correlation of the measured attributes and assumed or previously determined attributes for the respective the pathway elements.

17. The method of claim 16, wherein the cross-correlation includes executing an implementation of at least one of the following on the measured attributes and assumed or previously determined attributes for the respective the pathway elements:

multi-variant analysis, genetic algorithm, and inference reasoning.

18. The method of claim 1 wherein the statistical determination comprises executing statistical simulations.