Weight and diet attribute combination discovery

• US 8,065,324 B2
• Filed: 10/26/2010
• Issued: 11/22/2011
• Est. Priority Date: 03/16/2007
• Status: Active Grant

• Current Assignee: 23andme Incorporated (23andMe Holding Co.)
• Original Assignee: Expanse Networks, Inc.
• Inventors: Eldering, Charles Anthony, Kenedy, Andrew Alexander
• Primary Examiner(s): Jalil; Neveen Abel
• Assistant Examiner(s): MINCEY, JERMAINE A
• Application Number:
  US12/912,174
• Publication Number:
  US 20110040791A1
• Time in Patent Office: 392 Days
• Field of Search: None
• US Class Current: 707/769
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/2282 Tablespace storage structures; Management thereof
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 20/40 Population genetics; Linkage disequilibrium
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/20 : relating to practices or guidelines
• CPC Class Codes: H04L 67/306 : User profiles

Abstract

A method and system are presented in which a query attribute is used as the basis for accessing stored attribute combinations and their frequencies of occurrence for query-attribute-positive individuals and query-attribute-negative individuals and tabulating, based on frequencies of occurrence, those attribute combinations that are most likely to co-occur with the query attribute.

Claims

1. A computer program storage device readable by a machine and containing a set of instructions which, when read by the machine, causes execution of a bioinformatics method for generating a compilation containing combinations of pangenetic and non-pangenetic attributes that co-associate with a diet and weight related query attribute associated with an individual subject using the method, comprising:

a) receiving the diet and weight related query attribute which identifies, within a computer memory, a set of query-attribute-positive attribute profiles associated with query-attribute-positive individuals, defined as those individuals who have previously achieved the query attribute, and a set of query-attribute-negative attribute profiles associated with query-attribute-negative individuals, defined as those individuals who have not achieved the query attribute, wherein the respective attribute profiles comprise pangenetic attributes and non-pangenetic attributes;

b) accessing the respective attribute profiles of query-attribute-positive individuals and attribute profiles of query-attribute-negative individuals, wherein the attribute profiles comprise pangenetic attributes and non-pangenetic attributes, wherein the accessing includes choosing at least one query-attribute-positive attribute profile from the set of query-attribute-positive attribute profiles and at least one query-attribute-negative profile from the set of query-attribute-negative attribute profiles;

c) determining a set of candidate attributes comprising attributes from the at least one query-attribute-positive attribute profile of at least one query-attribute-positive individual that do not occur in the attribute profiles of at least a portion of the set of query-attribute-negative individuals attribute profiles;

d) determining the frequencies of occurrence of combinations of candidate attributes in the set of attribute profiles of the query-attribute-positive attribute profiles individuals and in the set of attribute profiles of the query-attribute-negative attribute profiles individuals, wherein each of the combinations of candidate attributes contains at least one pangenetic attribute and at least one non-pangenetic attribute selected from the set of candidate attributes;

e) storing, within a computer memory, the combinations of candidate attributes having higher frequencies of occurrence in the set of attribute profiles of the query-attribute-positive attribute profiles individuals than in the set of attribute profiles of the query-attribute-negative attribute profiles individuals to generate a compilation of attribute combinations that co-associate with the query attribute associated with said subject;

f) storing, in the compilation, corresponding statistical results that are computed based on the frequencies of occurrence to indicate the strength of association of each of the attribute combinations in the compilation with the query attribute; and

g) generating and transmitting for display a set of attribute combinations of pangenetic and non-pangenetic attributes that co-associate with the query attribute associated with said subject and which provide a pangenetic and non-pangenetic based weight related dietary recommendation in accord with combinations of pangenetic and non-pangenetic attributes co-associating with said subject.

2. The computer program storage device of claim 1, wherein the diet and weight related query is diet characteristics correlated with weight loss.

3. The computer program storage device of claim 1, wherein the diet and weight related query is diet characteristics correlated with weight gain.

4. The computer program storage device of claim 1, wherein the pangenetic and non-pangenetic based weight related dietary recommendation is a diet for weight loss wherein the diet is matched to the pangenetic and non-pangenetic attributes of the individual.

5. The computer program storage device of claim 1, wherein the pangenetic and non-pangenetic based weight related dietary recommendation is a diet for weight gain wherein the diet is matched to the pangenetic and non-pangenetic attributes of the individual.

6. A bioinformatics database system for generating a compilation containing combinations of pangenetic and non-pangenetic attributes that co-associate with a diet and weight related query of a subject, comprising:

a) a memory containing;

a first data structure containing attribute profiles of query-attribute-positive individuals, defined as those individuals who have previously achieved the query attribute, and attribute profiles of query-attribute-negative individuals, defined as those individuals who have not achieved the query attribute, wherein the respective attribute profiles comprise pangenetic attributes and non-pangenetic attributes;

b) a processor for;

i) receiving the diet and weight related query attribute which identifies, within the first data structure, a set of query-attribute-positive attribute profiles associated with query-attribute-positive individuals and a set of query-attribute-negative attribute profiles associated with query-attribute-negative individuals;

ii) accessing the first data structure and choosing at least one query-attribute-positive attribute profile from the set of query-attribute-positive attribute profiles and at least one query-attribute-negative attribute profile from the set of query-attribute-negative attribute profiles;

iii) determining a set of candidate attributes comprising attributes from the at least one query-attribute-positive attribute profile of at least one query-attribute-positive individual that do not occur in the attribute profiles of at least a portion of the subset of query-attribute-negative individuals attribute profiles;

iv) determining the frequencies of occurrence of combinations of candidate attributes in the set of attribute profiles of the query-attribute-positive attribute profiles individuals and in the set of attribute profiles of the query-attribute-negative attribute profiles individuals, wherein each of the combinations of candidate attributes contains at least one pangenetic attribute and at least one non-pangenetic attribute selected from the set of candidate attributes;

v) storing, to generate a second data structure in the memory based on statistical results to indicate strength of association, the combinations of candidate attributes having higher frequencies of occurrence in the set of attribute profiles of the query-attribute-positive attribute profile individuals than in the set of attribute profiles of the query-attribute-negative attribute profile individuals to generate a compilation of attribute combinations that co-associate with the query attribute; and

c) generating, and transmitting for display, based on the strength of association of the combinations of candidate attributes having higher frequencies of occurrence in the set of attribute profiles of the query-attribute-positive profile individuals than in the set of attribute profiles of the query-attribute-negative profiles individuals, a pangenetic and non-pangenetic based set of diet and weight attributes.

7. The bioinformatics database system of claim 6, wherein the processor is configured to request the diet and weight related query attribute in the form of a diet dependent weight loss goal.

8. The bioinformatics database system of claim 6, wherein the processor is configured to request the diet and weight related query attribute in the form of a diet dependent weight gain goal.

9. The bioinformatics database system of claim 6, wherein the processor is configured to generate the pangenetic and non-pangenetic based weight attributes in the form of recommended diet for weight loss.

10. The bioinformatics database system of claim 6, wherein the processor is configured to generate the pangenetic and non-pangenetic weight attributes in the form of recommended diet for weight gain.