METHODS AND SYSTEMS FOR HIGH CONFIDENCE UTILIZATION OF DATASETS

• US 20120095695A1
• Filed: 10/24/2011
• Published: 04/19/2012
• Est. Priority Date: 08/03/2005
• Status: Active Grant

• Current Assignee: ReSurfX, Inc.
• Original Assignee: ReSurfX, Inc.
• Inventors: Gopalan, Suresh
• Granted Patent: US 8,532,959 B2
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: G01J 1/58 using luminescence generated by light
• CPC Class Codes: G06F 11/004 Error avoidance G06F11/07 and subgroups take precedence
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Abstract

Methods and systems for high-confidence utilization of datasets are disclosed. In one embodiment, the method includes selecting a metric for determining substantially optimal combination of true positives and false positives in a data set, applying an optimization technique, and obtaining, from the results of the optimization technique, a value for at least one optimization parameter, the value for at least one optimization parameter resulting in substantially optimal combination of true positives and false positives. A number of true positives and a number of false positives are a function of the one or more optimization parameters.

Claims

1. A method for summarizing parameter value, the method being implemented in a computer and comprising the steps of:

grouping measurement result from a data set into a plurality of pairs of measurement results;

determining, for said each one pair of measurement results, whether predetermined measures for said each one pair of measurement results satisfy threshold criteria;

classifying a pair of measurement results from said plurality of pairs of measurement results as not changing if the predetermined measures do not satisfy the threshold criteria;

comparing, if the predetermined measures satisfied the threshold criteria, one measurement result in said each one pair of measurement results to another measurement result in said each one pair of measurement results;

classifying, after the comparison, said each one pair of measurement results according to result of the comparison;

selecting a common set of measurement results from the classified plurality of pairs of measurement result for use with the data set; and

providing summary measures for a parameter utilizing the common set;

said determining, classifying, comparing, selecting and providing being performed by means of a computer usable medium having computer readable code that causes the computer to perform said steps;

whereby the data set includes data obtained from large-scale measurements of organismal and cellular state involving multiple independent measurements of each parameter, said parameters including genes, transcripts and proteins; and

wherein the summarized parameter values are utilized for decision making to increase confidence on the use of the data in activities including manufacturing, handling, hybridization and gene expression.

2. The method of claim 1 further comprising the steps of:

providing summary measures of predetermined quantities utilizing the common set.

3. The method of claim 2 wherein one of the predetermined quantities is a confidence measure.

4. The method of claim 1 wherein the data set is a replicated data set;

and wherein the method further comprises the step of;

averaging, as a first step, the measurement results over replications;

averaged measurement results constituting said measurement results.

5. The method of claim 4 further comprising the step of:

providing summary measures of predetermined quantities utilizing the common set.

6. The method of claim 5 wherein one of the predetermined quantities is a statistical measure.

7. A method for determining efficacy of a data set analyses techniques, the method being implemented in a computer and comprising the steps of:

scaling a data set and replicates of the data set;

comparing the scaled data set to predetermined groups of original data sets in order to test a predetermined analysis results;

wherein efficacy can be determined;

said scaling and comparing being performed by means of a computer usable medium having computer readable code that causes the computer to perform said steps;

whereby the data set being analyzed includes data obtained from large-scale measurements of organismal and cellular state involving multiple independent measurements of each parameter, said parameters including genes, transcripts and proteins; and

wherein the efficacy of a data set analyses techniques is utilized for decision making to increase confidence on the use of the data in activities including manufacturing, handling, hybridization, gene expression.

8. A computer program product comprising a computer usable medium having computer readable code embodied therein;

said computer readable code being capable of causing a computer system to;

scale a data set and replicates of the data set; and

compare the scaled data set to predetermined groups of original data sets in order to test a predetermined analysis results;

wherein efficacy can be determined;

whereby the data set being analyzed includes data obtained from large-scale measurements of organismal and cellular state involving multiple independent measurements of each parameter, said parameters including genes, transcripts and proteins; and

wherein the efficacy of a data set analyses techniques is utilized for decision making to increase confidence on the use of the data in activities including manufacturing, handling, hybridization and gene expression.