DATA MINING PLATFORM FOR BIOINFORMATICS AND OTHER KNOWLEDGE DISCOVERY

• US 20080097939A1
• Filed: 10/30/2007
• Published: 04/24/2008
• Est. Priority Date: 05/01/1998
• Status: Active Grant

• 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: Curtis Anderson, Garry L. Carls, Glynn Bergeron, James Roberts, Joe Mckenzie, John E. Matthews, Jules B. Paderewski, Julian N. Stern, Memorial Health Systems Incorporated, Peter J. Farley, Simpson K. Russell, Timothy P. O'Hayer
• Inventors: Lewis, David, Reiss, Edward, Doursat, Rene, Guyon, Isabelle, Weston, Jason
• Granted Patent: US 7,542,947 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• 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 18/2411 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: 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: 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 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• CPC Class Codes: G16B 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/20 Heterogeneous data integration
• CPC Class Codes: Y10S 707/99936 Pattern matching access

Abstract

The data mining platform comprises a plurality of system modules, each formed from a plurality of components. Each module has an input data component, a data analysis engine for processing the input data, an output data component for outputting the results of the data analysis, and a web server to access and monitor the other modules within the unit and to provide communication to other units. Each module processes a different type of data, for example, a first module processes microarray (gene expression) data while a second module processes biomedical literature on the Internet for information supporting relationships between genes and diseases and gene functionality. In the preferred embodiment, the data analysis engine is a kernel-based learning machine, and in particular, one or more support vector machines (SVMs). The data analysis engine includes a pre-processing function for feature selection, for reducing the amount of data to be processed by selecting the optimum number of attributes, or “features”, relevant to the information to be discovered.

Claims

1. A computer-implemented data mining platform for generating an output comprising knowledge from analysis of a plurality of heterogeneous data types, the platform comprising:

a plurality of modules, each module adapted for processing one data type of the plurality of heterogeneous data types, each module comprising an input data source, a data analysis engine, a data output and a web server connection for each of the input data source, the data analysis engine and the data output;

a web server connected to the web server connection for communicating with each of the input data source, the data analysis engine and the data output and for providing means for monitoring one or more of the input data source, the data analysis engine, and the data output; and

a combined data analysis engine in communication with the web server for combining the data output from the plurality of modules to generate a single output representing results obtained from analyzing the plurality of heterogeneous data types.

2. The data mining platform of claim 1, wherein the data analysis engine comprises a processor for executing at least one support vector machine.

3. The data mining platform of claim 2, wherein the processor further executes at least one pre-processing algorithm for pre-processing the input data.

4. The data mining platform of claim 3, wherein pre-processing algorithm comprising a feature selection algorithm.

5. The data mining platform of claim 4, wherein the feature selection algorithm comprises recursive feature elimination.

6. The data mining platform of any one of claims 2 through 5, wherein the data analysis engine includes a visualization tool for displaying the results.

7. The data mining platform of any one of claims 1 through 6, wherein one data type comprises gene expression data and wherein the data analysis engine generates a ranked list of genes.

8. The data mining platform of claim 7, wherein the data analysis engine generate a gene observation tree for visually displaying the ranked list of genes.

9. The data mining platform of claim 7, wherein a second type of data comprises knowledge contained in published literature and wherein the combined data analysis engine validates the ranked list of genes according to the knowledge contained in published literature.