INFERRING BIOLOGICAL PATHWAYS FROM UNSTRUCTURED TEXT ANALYSIS

• US 20150220680A1
• Filed: 01/31/2014
• Published: 08/06/2015
• Est. Priority Date: 01/31/2014
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: BOYER, STEPHEN K, KREULEN, JEFFREY T, SPANGLER, W SCOTT
• Application Number:
  US14/170,373
• Publication Number:
  US 20150220680A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks

Abstract

A biological pathway is a series of actions that take place in an organism that lead to some resulting pathology or otherwise change the organism state. In the cell, these actions typically take place between molecules called proteins. Proteins within the cell interact in ways that are not fully understood, but evidence concerning these interactions is constantly being collected and published by microbiologists. The disclosed method automatically infers such biological pathways between proteins by looking at the overall system of published literature about those proteins.

Claims

1. A method for discovering a pathway among a set of biological and/or chemical entities, comprising:

a) providing documents about each of the biological and/or chemical entities;

b) creating a vector space representation of the documents based on words and/or phrases occurring in the documents;

c) for each biological and/or chemical entity, creating a centroid in the vector space based on the vectors corresponding to documents mentioning that biological and/or chemical entity;

d) creating a relative distance network of the biological and/or chemical entities, in view of the centroids, thereby identifying a particular pathway connecting the centroids; and

e) finding at least one most connected centroid on said particular pathway, thereby identifying a particular biological and/or chemical entity for further investigation, wherein said particular biological and/or chemical entity corresponds to said at least one most connected centroid.

2. The method of claim 1, wherein, prior to step (b), documents matching more than one biological and/or chemical entity are removed.

3. The method of claim 1, wherein biological and/or chemical entities having less than a pre-defined threshold number of documents are removed.

4. The method of claim 1, wherein said biological and/or chemical entities are selected from the group consisting of human genes and proteins.

5. The method of claim 1, wherein said documents are provided in response to a query.

6. The method of claim 1, wherein said documents are provided over a network.

7. The method of claim 6, wherein said network is any of the following:

local area network (LAN), wide area network (WAN), the Internet, or cellular network.

8. A method comprising:

a. receiving a set of biological and/or chemical entities of interest, E;

b. identifying a document set, R, mentioning any biological and/or chemical entity, and/or a variant thereof in E;

c. creating a dictionary, D, from common terms and/or phrases in documents of document set R;

d. assigning each document in document set R a numeric vector using a vector space model based on said dictionary D;

e. computing a centroid for each biological and/or chemical entity in E by averaging numerical vectors of documents in R mentioning that biological and/or chemical entity;

f. computing a distance matrix listing a distance between pairs of centroids;

g. creating a relative neighborhood graph of biological and/or chemical entities in E based on said computed distance matrix, said relative neighborhood graph identifying a particular pathway connecting computed centroids; and

h. identifying, from said relative neighborhood graph, at least one most connected centroid and outputting biological and/or chemical entity associated with said at least one most connected centroid.

9. The method of claim 8, wherein said step of creating said relative neighborhood graph comprises:

g1. creating a candidate set, C, with biological and/or chemical entities in E;

g2. selecting an initial biological and/or chemical entity in C as a new node, e, to add to a tree and removing said new node e from C;

g3. comparing remaining biological and/or chemical entities in C to identify another biological and/or chemical entity to add to said tree with a shortest distance to existing nodes in said tree and adding said identified another biological and/or chemical entity to said tree and removing said another node from C,

wherein step g3 is iteratively repeated for other entries in C until there are no more entries in C, with all entries in C being added to said tree; and

resulting tree is output as part of said relative neighborhood graph.

10. The method of claim 8, wherein said distance between pairs of centroids is a cosine distance.

11. The method of claim 8, wherein, prior to step (c), documents in R matching more than one biological and/or chemical entity in E are removed from R.

12. The method of claim 8, wherein, prior to step (c), biological and/or chemical entities in E having less than a pre-defined threshold number of documents are removed from E.

13. The method of claim 8, wherein said method comprises displaying said computed centroids and documents in document set, R, via a scatter plot graph.

14. The method of claim 8, wherein said biological and/or chemical entities are selected from the group consisting of human genes and proteins.

15. The method of claim 8, wherein said document set R is identified in response to a query.

16. The method of claim 8, wherein said document set R is identified over a network.

17. The method of claim 16, wherein said network is any of the following:

local area network (LAN), wide area network (WAN), the Internet, or cellular network.

18. A non-transitory, computer accessible memory medium storing program instructions for discovering a pathway among a set of biological and/or chemical entities, wherein the program instructions are executable by a processor to:

a. receive a set of biological and/or chemical entities of interest, E;

b. identify a document set, R, mentioning any biological and/or chemical entity, and/or a variant thereof in E;

c. create a dictionary, D, from common terms and/or phrases in documents of document set R;

d. assign each document in document set R a numeric vector using a vector space model based on said dictionary D;

e. compute a centroid for each biological and/or chemical entity in E by averaging numerical vectors of documents in R mentioning that biological and/or chemical entity;

f. compute a distance matrix listing a distance between pairs of centroids;

g. create a relative neighborhood graph of biological and/or chemical entities in E based on said computed distance matrix, said relative neighborhood graph identifying a particular pathway connecting computed centroids; and

h. identify, from said relative neighborhood graph, at least one most connected centroid and outputting biological and/or chemical entity associated with said at least one most connected centroid.

19. A system for discovering a pathway among a set of biological/chemical entities, the system comprising:

one or more processors; and

a memory comprising instructions which, when executed by the one or more processors, cause the one or more processors to;

a. receive a set of biological and/or chemical entities of interest, E;

b. identify a document set, R, mentioning any biological and/or chemical entity, and/or a variant thereof in E;

c. create a dictionary, D, from common terms and/or phrases in documents of document set R;

d. assign each document in document set R a numeric vector using a vector space model based on said dictionary D;

e. compute a centroid for each biological and/or chemical entity in E by averaging numerical vectors of documents in R mentioning that biological and/or chemical entity;

f. compute a distance matrix listing a distance between pairs of centroids;

g. create a relative neighborhood graph of biological and/or chemical entities in E based on said computed distance matrix, said relative neighborhood graph identifying a particular pathway connecting computed centroids; and

h. identify, from said relative neighborhood graph, at least one most connected centroid and outputting biological and/or chemical entity associated with said at least one most connected centroid.