System for identifying patterns in biological data using a distributed network

• US 6,714,925 B1
• Filed: 08/07/2000
• Issued: 03/30/2004
• Est. Priority Date: 05/01/1999
• Status: Expired due to Term

• Current Assignee: Curtis Anderson, Health Discovery Corporation, James Roberts, Joe Mckenzie, Jules B. Paderewski, Julian N. Stern, Memorial Health Systems Incorporated, Timothy P. O'Hayer
• Original Assignee: Barnhill Technologies, LLC
• Inventors: Barnhill, Stephen, Guyon, Isabelle, Weston, Jason
• Primary Examiner(s): Davis, George B.
• Application Number:
  US09/633,627
• Time in Patent Office: 1,331 Days
• Field of Search: 706/16, 706/48
• US Class Current: 706/48
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06F 18/2113 by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
• CPC Class Codes: G06F 18/2115 by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/771 Feature selection, e.g. selecting representative features from a multi-dimensional feature space
• CPC Class Codes: G06V 10/774 Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• 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 40/30 Unsupervised data analysis
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• 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

System for enhancing knowledge discovery from data using a learning machine in general and a support vector machine in particular in a distributed network environment. A customer transmits training data, test data and live data to a vendor'"'"'s server from a remote source, via a distributed network. The training biological data, test biological data and live biological data is stored in a storage device. Training biological data is then pre-processed in order to add meaning thereto. Pre-processing data involves transforming the biological data points and/or expanding the biological data points. Live biological data is pre-processed and input into the trained and tested learning machine. The live output from the learning machine is then post-processed into a computationally derived alphanumerical classifier for interpretation by a human or computer automated process.

Claims

1. A system for identifying patterns in biological data using a support vector machine comprising:

a server in communication with a distributed network for receiving a training biological data set, a test biological data set, a live biological data set and a financial account identifier from one or more remote sources, the one or more remote sources also in communication with the distributed network;

one or more storage devices in communication with the server for storing the training biological data set and the test biological data set; and

a processor for executing a support vector machine operable and for;

collecting the training biological data set from the one or more storage devices,

pre-processing the training biological data set to add meaning to each of a plurality of training biological data points,

inputting the pre-processed training biological data set into the support vector machine so as to train the support vector machine,

in response to training of the support vector machine, collecting the test biological data set from the one or more storage devices,

pre-processing the test biological data set to add meaning to each of a plurality of test biological data points,

inputting the test biological data set into the trained support vector machine in order to test the support vector machine,

in response to receiving a test output from the trained support vector machine, collecting the live biological data set from the one or more storage devices,

inputting the live biological data set into the tested and trained support vector machine in order to process the live biological data,

in response to receiving a live output from the support vector machine, post-processing the live output, and

transmitting the post-processed live output to the server;

wherein the server is further operable for;

communicating with a financial institution in order to a financial account identified by the financial account identifier, and

in response to securing payment, transmitting the post-processed live output to the one or more remote sources or another remote source.

2. The system of claim 1, wherein each training biological data point comprises a vector having one or more coordinates;

and

wherein pre-processing the training biological data set to add meaning to each training biological data point comprises;

determining that the training biological data point is dirty; and

in response to determining that the training biological data point is dirty, cleaning the training biological data point.

3. The system of claim 2, wherein cleaning the training biological data point comprises deleting, repairing or replacing the biological data point.

4. The system of claim 1, wherein each training biological data point comprises a vector having one or more original coordinates;

and

wherein pre-processing the training biological data set to add meaning to each training biological data point comprises adding dimensionality to each training biological data point by adding one or more new coordinates to the vector.

5. The system of claim 4, wherein the one or more new coordinates added to the vector are derived by applying a transformation to one or more of the original coordinates.

6. The system of claim 4, wherein the transformation is based on expert knowledge.

7. The system of claim 4, wherein the transformation is computationally derived.

8. The system of claim 4, wherein the training biological data set comprises a continuous variable;

and

wherein the transformation comprises optimally categorizing the continuous variable of the training biological data set.

9. The system of claim 1, wherein the knowledge to be discovered from the biological data relates to a regression or density estimation;

wherein the support vector machine produces a training output comprising a continuous variable; and

wherein the processor is further operable for post-processing the training output by optimally categorizing the training output to derive cutoff points in the continuous variable.

10. The system of claim 1, wherein the processor is further operable for:

in response to comparing each of the test outputs with each other, determining that none of the test outputs is the optimal solution;

adjusting the different kernels of one or more of the plurality of support vector machines; and

in response to adjusting the selection of the different kernels, retraining and retesting each of the plurality of support vector machines.

11. The system of claim 1, wherein the post-processed live output comprises a computationally derived alpha-numerical classifier adapted for human or automated interpretation.

12. A system for diagnosing disease using a support vector machine comprising:

a server in communication with a distributed network for receiving a training biological data set, a test biological data set, a live biological data set and a financial account identifier from a one or more remote sources, the one or more remote sources also in communication with the distributed network;

one or more storage devices in communication with the server for storing the training biological data set and the test biological data set; and

a processor for executing a support vector machine and for;

collecting the training biological data set from the one or more storage devices,

pre-processing the training biological data set to add meaning to each of a plurality of training biological data points,

inputting the pre-processed training biological data set into the support vector machine so as to train the support vector machine,

in response to training of the support vector machine, collecting the test biological data set from the database one or more storage devices,

pre-processing the test biological data set to add meaning to each of a plurality of test biological data points,

inputting the test biological data set into the trained support vector machine in order to test the support vector machine,

in response to receiving a test output from the trained support vector machine, collecting the live biological data set from the one or more storage devices,

inputting the live biological data set into the tested and trained support vector machine in order to process the live biological data,

in response to receiving a live output from the support vector machine, post-processing the live output, and

transmitting the post-processed live output to the server;

wherein the server is further operable for;

communicating with a financial institution in order to secure payment through a financial account identified by the financial account identifier, and

in response to securing payment, transmitting the post-processed live output to the one or more remote sources or another remote source.

13. The system of claim 12, wherein the disease is cancer.

14. The meshed system of claim 13, wherein the cancer is colon cancer.

15. The method system of claim 13, wherein the cancer is breast cancer.

16. The system of claim 12, wherein the post-processed live output comprises a computationally derived alpha-numerical classifier adapted for human or automated interpretation.