Semantic gene organizer

• US 20060047441A1
• Filed: 08/30/2005
• Published: 03/02/2006
• Est. Priority Date: 08/31/2004
• Status: Abandoned Application

• Current Assignee: University of Tennessee Research Foundation (University of Teesside)
• Original Assignee: University of Tennessee Research Foundation (University of Teesside)
• Inventors: Wei, Lai, Heinrich, Kevin Erich, Homayouni, Ramin, Berry, Michael Waitsel
• Application Number:
  US11/215,635
• Publication Number:
  US 20060047441A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/10 Ontologies; Annotations

Abstract

A semantic gene classification and annotation system, method and computer program can utilize Latent Semantic Indexing (LSI) to identify conceptually related genes based on textual information in biomedical literature, including MEDLINE citations. In addition, term weights calculated from the usage of the gene terms in and across gene documents can be used to automatically assign gene aliases and extend gene function annotation based upon primary biomedical literature.

Claims

1. A sementic gene organization method comprising:

producing at least one gene document for a plurality of selected genes by compiling textual information for citations which are cross-referenced in a database for said selected genes;

processing said gene documents according to a latent semantic indexing (LSI) model to measure similarities between gene documents based upon similar word usage patterns; and

,

parsing said gene documents to produce a result set of semantically relevant gene relationships responsive to receiving a query vector of at least one term.

2. The method of claim 1, wherein said producing at least one gene document for a plurality of selected genes by compiling textual information for citations which are cross-referenced in a database for said selected genes, further comprises:

assembling and parsing said textual information into a dictionary of terms and weighted frequencies; and

,

generating a term-by-gene matrix with said dictionary of terms.

3. The method of claim 2, wherein said assembling and parsing said textual information into a dictionary of terms and weighted frequencies, further comprises imposing restrictions upon term frequencies in said dictionary to control dictionary size.

4. The method of claim 2, wherein said generating a term-by-gene matrix with said dictionary of terms, further comprises applying to said term-by-gene matrix a weighting to decrease weights of high-frequency terms while giving distinguishing terms higher weight.

5. The method of claim 4, wherein said applying to said matrix weighting to decrease weights of high-frequency terms while giving distinguishing terms higher weight, comprises using values of said terms to define specific gene descriptors to extend gene function annotations.

6. The method of claim 1, wherein said processing said gene documents according to an LSI model to measure similarities between gene documents based upon similar word usage patterns, comprises generating term and document vectors for said LSI model by truncating a singular value decomposition (SVD) of said term-by-gene document matrix to s factors to produce a rank-reduced space in which to compare two gene-documents at different conceptual levels.

7. The method of claim 1, wherein said parsing said gene documents to produce a result set of semantically relevant gene relationships responsive to receiving a query vector of at least one term, comprises:

determining a relevance to said at least one term by ranking a similarity score, defined by a cosine of a vector angle between said query vector and said gene-documents; and

,

generating a ranked list of genes based upon an angle of said gene documents and said query vector.

8. The method of claim 1, further comprising producing said query vector according to one of a keyword query and a gene document query.

9. A semantic gene organization data processing system comprising:

a term-by-gene matrix generator configured to generate a term-by-gene document matrix based upon terms identified within gene documents;

singular value decomposition (SVD) logic enabled to generate a plurality of factor matrices based upon said term-by-gene document matrix; and

,

a document-to-document similarity processor having a configuration to receive said factor matrices and to generate one of similarity and distance scores based upon a received query vector to produce results for said query vector.

10. The system of claim 9, further comprising a parser coupled to a pre-processor enabled to identify said terms within said gene documents.

11. The system of claim 9, further comprising ranking logic enabled to rank said results for said query vector.

12. The system of claim 9, further comprising clustering logic enabled to cluster said results for said query vector based upon a gene-by-gene distance matrix produced by said matrix generator.

13. A computer program product comprising a computer usable medium having computer usable program code for sementic gene organization, said computer program product including:

computer usable program code for producing at least one gene document for a plurality of selected genes by compiling textual information for citations which are cross-referenced in a database for said selected genes;

computer usable program code for processing said the gene documents according to a latent semantic indexing (LSI) model to measure similarities between gene documents based upon similar word usage patterns; and

,

computer usable program code for parsing said gene documents to produce a result set of semantically relevant gene relationships responsive to receiving a query vector of at least one term.

14. The computer program product of claim 13, wherein said computer usable program code for producing at least one gene document for a plurality of selected genes by compiling textual information for citations which are cross-referenced in a database for said selected genes, further comprises:

computer usable program code for assembling and parsing said textual information into a dictionary of terms and weighted frequencies; and

,

computer usable program code for generating a term-by-gene matrix with said dictionary of terms.

15. The computer program product of claim 14, wherein said computer usable program code for assembling and parsing said textual information into a dictionary of terms and weighted frequencies, further comprises computer usable program code for imposing restrictions upon term frequencies in said dictionary to control dictionary size.

16. The computer program product of claim 14, wherein said computer usable program code for generating a term-by-gene matrix with said dictionary of terms, further comprises computer usable program code for applying a weighting to decrease weights of high-frequency terms while giving distinguishing terms higher weight.

17. The computer program product of claim 16, wherein said computer usable program code for applying to said matrix a weighting to decrease weights of high-frequency terms while giving distinguishing terms higher weight, comprises computer usable program code for using weighted values of said terms to define specific gene descriptors to extend gene function annotations.

18. The computer program product of claim 13, wherein said computer usable program code for processing said gene documents according to an LSI model to measure similarities between gene documents based upon similar word usage patterns, comprises computer usable program code for generating term and document vectors for said LSI model by truncating a singular value decomposition (SVD) of said term-by-gene document matrix to s factors to produce a rank-reduced space in which to compare two gene-documents at different conceptual levels.

19. The computer program product of claim 13, wherein said computer usable program code for parsing said gene documents to produce a result set of semantically relevant gene relationships responsive to receiving a query vector of at least one term, comprises:

computer usable program code for determining a relevance to said at least one term by ranking a similarity score, defined by a cosine of a vector angle between a query vector and gene-document vectors; and

,

computer usable program code for determining a relevance to said at least one term by ranking a distance score, defined by 1 minus the cosine of a vector angle between said query vector and said gene-document vectors; and

,

computer usable program code for generating a ranked list of genes based upon an angle of said gene documents and said query vector.

20. The computer program product of claim 13, further comprising computer usable program code for producing said query vector according to one of a keyword query and a gene document query.