1. A computer-implemented method comprising:
receiving data associated with a co-occurrence graph among heterogeneous entities, said co-occurrence graph comprising a plurality of nodes, each node representing an entity in said heterogeneous entities, wherein any two nodes in said co-occurrence graph are connected by an edge when they co-occur in a knowledge base, with a weight of said edge being equal to the number of times entities associated with said two nodes co-occur in said knowledge base;
receiving a query comprising a query entity name and a target entity type;
receiving a plurality of meta paths to constrain co-occurrence scope of any two heterogeneous entities in said co-occurrence graph;
generating a subgraph of said co-occurrence graph with path instances of said received meta paths; and
outputting entities from said subgraph belonging to said target entity type and having strong relevance with said query entity name based on a probabilistic context-aware relevance model, where said strong relevance is constrained by said received meta paths.
2. The computer-implemented method of claim 1, wherein said query entity name is a disease name and said target entity type is \u201cDrug\u201d.
3. The computer-implemented method of claim 1, wherein said data associated with said co-occurrence graph is built from a plurality of the following: FDA-approved drugs, diseases extracted from human disease ontology, small-molecule chemical compounds with drug indications from a first database, terms in a tree used as a metadata to index documents in a second database, and targets made up of four sub-types: tissue, cell-line, protein, and organism.
4. The computer-implemented method of claim 1, wherein said received meta paths are any of, or a combination of, the following: \u201cDrug-Disease\u201d, \u201cDrug-Drug-Disease\u201d, \u201cDrug-Compound-Disease\u201d, \u201cDrug-Disease-Disease\u201d and \u201cDrug-MeSH Term-Disease\u201d.
5. The computer-implemented method of claim 1, wherein said heterogeneous entities are selected from any of the following: drug, compound, disease, target, and Medical Subject Headings (MeSH).
6. The computer-implemented method of claim 1, wherein said heterogeneous entities are heterogeneous biological andor chemical entities.
7. The computer-implemented method of claim 1, wherein said knowledge base is accessible over a network.
8. The computer-implemented method of claim 7, wherein said network is any of the following: local area network (LAN), wide area network (WAN), the Internet, or cellular network.
9. A non-transitory, computer accessible memory medium storing program instructions for mining strong relevance between heterogeneous entities from their co-occurrences comprising:
computer readable program code receiving data associated with a co-occurrence graph among heterogeneous entities, said co-occurrence graph comprising a plurality of nodes, each node representing an entity in said heterogeneous entities, wherein any two nodes in said co-occurrence graph are connected by an edge when they co-occur in a knowledge base, with a weight of said edge being equal to the number of times entities associated with said two nodes co-occur in said knowledge base;
computer readable program code receiving a query comprising a query entity name and a target entity type;
computer readable program code receiving a plurality of meta paths to constrain co-occurrence scope of any two heterogeneous entities in said co-occurrence graph;
computer readable program code generating a subgraph of said co-occurrence graph with path instances of said received meta paths; and
computer readable program code outputting entities from said subgraph belonging to said target entity type and having strong relevance with said query entity name based on a probabilistic context-aware relevance model, where said strong relevance is constrained by said received meta paths.
10. A method comprising:
receiving a co-occurrence graph among different entities, wherein (i) each node in said co-occurrence graph represents an entity and (ii) two nodes in said co-occurrence graph are connected by an edge if they occur together in a document within a collection of documents, and wherein a weight on each edge equals the number of times two entities occur together in said collection of documents;
receiving a query comprising a query entity name and a target entity type;
receiving pre-specified meta paths to constrain a scope of co-occurrence between two different entities in said co-occurrence graph; and
outputting entities that (i) belong to said target entity type, and (ii) are functionally relevant to an instance of said query entity name.
11. The method of claim 10, comprising:
building a probabilistic context-aware relevance model to measure said functional relevance between said query entity name and said target entity type, in view of said scope, by:
(i) profiling said query entity name using a first set of adjacent entities within said scope;
(ii) profiling said target entity type using a set of adjacent entities within said scope;
(iii) wherein said functional relevance between said query entity name and said target entity type is a weighted product of the functional relevance between all pairs of adjacent entities, wherein one entity comes from said first set of adjacent entities and the other entity comes from said second set of adjacent entities; and
(iv) iteratively computing the functional relevance between any pair of adjacent entities according to steps (i), (ii), and (iii);
wherein said weight in step (iii) measures an inverse document frequency (IDF) based importance of adjacent entities to said query entity name and said target entity type.
12. The computer-implemented method of claim 10, wherein said query entity name is a disease name and said target entity type is \u201cDrug\u201d.
13. The method of claim 10, wherein data associated with said co-occurrence graph are built from a plurality of the following: FDA-approved drugs, diseases extracted from human disease ontology, small-molecule chemical compounds with drug indications from a first database, terms in a tree used as a metadata to index documents in a second database, and targets made up of four sub-types: tissue, cell-line, protein, and organism.
14. The method of claim 10, wherein said received meta paths are any of, or a combination of, the following: \u201cDrug-Disease\u201d, \u201cDrug-Drug-Disease\u201d, \u201cDrug-Compound-Disease\u201d, \u201cDrug-Disease-Disease\u201d and \u201cDrug-MeSH Term-Disease\u201d.
15. The method of claim 10, wherein said heterogeneous entities are selected from any of the following: drug, compound, disease, target, and Medical Subject Headings (MeSH).
16. The method of claim 10, wherein said heterogeneous entities are heterogeneous biological andor chemical entities.
17. The method of claim 10, wherein said collection of documents are accessible over a network.
18. The method of claim 18, wherein said network is any of the following: local area network (LAN), wide area network (WAN), the Internet, or cellular network.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A biocompatible tissue repair stimulating implant, comprising:
a bioabsorbable polymeric foam component having pores with an open cell pore structure;
a reinforcing component formed of a biocompatible, mesh-containing material, wherein the foam component is integrated with the reinforcing component such that the pores of the foam component penetrate the mesh of the reinforcing component and interlock with the reinforcing component; and
at least one biological component in association with the implant, wherein the biological component is a growth factor or a source of growth factors.
2. The implant of claim 1, wherein the biological component is contained within pores of the foam component.
3. The implant of claim 1, wherein the source of growth factors includes platelets.
4. The implant of claim 1, wherein the biological component further includes an activator of platelets.
5. The implant of claim 4, wherein the activator of platelets is selected from the group consisting of thrombin, adenosine di-phosphate (ADP), collagen, epinephrine, arachidonic acid, ristocetin, and combinations thereof.
6. The implant of claim 1, wherein the growth factor is selected from the group consisting of a transforming growth factor-\u03b2, bone morphogenic protein, cartilage derived morphogenic protein, growth differentiation factor, fibroblast growth factor, platelet-derived growth factor, vascular endothelial cell-derived growth factor, epidermal growth factor, insulin-like growth factor, hepatocyte growth factor, and fragments and combinations thereof.
7. The implant of claim 1, wherein the growth factor is autologous.
8. The implant of claim 3, wherein the platelets are delivered to the implant with a material selected from the group consisting of plasma and fibrinogen.
9. The implant of claim 3, wherein the platelets are delivered in the form of a platelet rich plasma clot.
10. The implant of claim 3 wherein the platelets are delivered in a biological or synthetic hydrogel selected from the group consisting of alginate, cross-linked alginate, hyaluronic acid, collagen gel, fibrin glue, fibrin clot, poly(N-isopropylacrylamide), agarose, chitin, chitosan, cellulose, polysaccharides, poly(oxyalkylene), a copolymer of poly(ethylene oxide)-poly(propylene oxide), poly(vinyl alcohol), polyacrylate, platelet poor plasma (PPP) clot, laminin, solubilized basement membrane, and combinations thereof.
11. The implant of claim 1, wherein the foam component is present in one or more layers.
12. The implant of claim 11, wherein adjacent foam layers are integrated with one another by at least a partial interlocking of pores.
13. The implant of claim 1, wherein the reinforcing component is present in one or more layers.
14. The implant of claim 11, wherein separate foam layers are constructed of different polymers.
15. The implant of claim 14, wherein the properties of the foam component vary throughout a thickness dimension of the implant.
16. The implant of claim 15, wherein outer layers of the implant have a greater overall pore volume than does an inner region thereof.
17. The implant of claim 15, wherein an inner region of the implant has a greater overall pore volume than do outer layers of the implant.
18. The implant of claim 16, wherein the concentration of the biological component is greater in the outer layers than in the inner region.
19. The implant of claim 17, wherein the concentration of the biological component is greater in the inner region than in the outer layers.