Method of analysing representations of separation patterns

• US 20070016606A1
• Filed: 08/26/2005
• Published: 01/18/2007
• Est. Priority Date: 07/15/2005
• Status: Abandoned Application

• Current Assignee: Biosignatures Limited
• Original Assignee: Biosignatures Limited
• Inventors: Bramwell, David, Morns, Ian, Kapferer, Anna
• Application Number:
  US11/212,479
• Publication Number:
  US 20070016606A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/23211 with adaptive number of clusters
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• 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

Abstract

The present invention relates principally to the statistical analysis of protein separation patterns. The invention provides a method of analysing representations of separation patterns, the method comprising iteratively performing the steps of (1) building a classification model based on a subset of data points selected from one or more representations, (2) assessing the performance of the model to determine whether its performance is within a desired range, and (3) adjusting the size of the subset until the performance of the model falls within the desired range.

Claims

1. A method of performing operations on protein samples for the analysis of representations of separation patterns, the method comprising iteratively performing the steps of (1) building a classification model based on a subset of data points selected from one or more representations, (2) assessing the performance of the model to determine whether its performance is within a desired range, and (3) adjusting the size of the subset until the performance of the model falls within the desired range.

2. The method of claim 1 wherein, during each iteration, steps (1) and (2) are repeated for subsets of uniform size but including different data points to obtain a distribution of model performances.

3. The method of claim 1 wherein step (2) includes determining whether a mean performance of the distribution is within the desired range.

4. The method of claim 1 wherein step (3) includes reducing the size of the subset if the mean performance is between a higher end of the desired range and perfect performance.

5. The method of claim 1 wherein step (3) includes increasing the size of the subset if the mean performance is below a lower end of the desired range.

6. The method of claim 1 wherein the desired range is from about 2.5 to about 3.0 standard deviations below perfect performance.

7. The method of claim 1 wherein, during the first iteration, step (1) includes arbitrarily selecting the size of the subset.

8. The method of claim 1 wherein, in step (1), the data points forming the subset are selected randomly.

12. A computer program directly loadable into the internal memory of a digital computer, comprising software code portions for performing the method of claim 1 when said program is run on the digital computer.

13. A computer program product directly loadable into the internal memory of a digital computer, comprising software code portions for performing the method of claim 1 when said product is run on the digital computer.

14. A carrier, which may comprise electronic signals, for a computer program of claim 13.

15. Electronic distribution of a computer program of claim 13.

9. A method of analysing representations of separation patterns, the method comprising iteratively performing the steps of (1) building a classification model based on a subset of data points selected from one or more representations, (2) assessing the performance of the model to determine whether its performance is within a desired range, and (3) adjusting the size of the subset until the performance of the model falls within the desired range.

10. Apparatus for performing operations on protein samples for the analysis of representations of separation patterns, the apparatus comprising means for iteratively performing the steps of (1) building a classification model based on a subset of data points selected from one or more representations, (2) assessing the performance of the model to determine whether its performance is within a desired range, and (3) adjusting the size of the subset until the performance of the model falls within the desired range.

11. Apparatus for analysing representations of separation patterns, the apparatus comprising means for iteratively performing the steps of (1) building a classification model based on a subset of data points selected from one or more representations, (2) assessing the performance of the model to determine whether its performance is within a desired range, and (3) adjusting the size of the subset until the performance of the model falls within the desired range.

16. A computer-readable medium having computer executable instructions for performing a method of performing operations on protein samples for the analysis of representations of separation patterns, the method comprising iteratively performing the steps of (1) building a classification model based on a subset of data points selected from one or more representations, (2) assessing the performance of the model to determine whether its performance is within a desired range, and (3) adjusting the size of the subset until the performance of the model falls within the desired range.