Reasoning on data model for disease monitoring, characterization and investigation

• US 10,311,363 B1
• Filed: 12/30/2015
• Issued: 06/04/2019
• Est. Priority Date: 04/06/2015
• Status: Active Grant

• Current Assignee: Emc IP Holding Company LLC (Dell Technologies Inc.)
• Original Assignee: Emc IP Holding Company LLC (Dell Technologies Inc.)
• Inventors: Florissi, Patricia Gomes Soares, Ukelson, Michal Ziv, Dach, Ran, Benshahar, Arnon
• Primary Examiner(s): Chen, Alan
• Application Number:
  US14/983,932
• Time in Patent Office: 1,252 Days
• Field of Search: None
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 30/10 Sequence alignment; Homology search
• CPC Class Codes: G16B 30/20 Sequence assembly
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• 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
• CPC Class Codes: H04L 43/065 related to network devices
• CPC Class Codes: H04L 47/70 Admission control; Resource allocation
• CPC Class Codes: H04L 47/762 triggered by the network
• CPC Class Codes: H04L 47/783 Distributed allocation of resources, e.g. bandwidth brokers
• CPC Class Codes: H04L 47/827 Aggregation of resource allocation or reservation requests
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

A method comprises processing metagenomics sequencing results from a plurality of metagenomics sequencing centers associated with respective data zones, configuring a data model based at least in part on the metagenomics sequencing results, performing one or more reasoning operations over the data model to infer relationships between entities of the data model that are not directly expressed by the data model, and updating the data model based at least in part on the inferred relationships. The method further comprises repeating the processing, configuring, performing and updating for additional metagenomics sequencing results from one or more of the metagenomics sequencing centers. One or more portions of the data model illustratively comprise respective profiles each characterizing at least one of a disease, infection or contamination based at least in part on the metagenomics sequencing results.

Claims

1. A method comprising:

configuring a first processing node for communication with one or more additional processing nodes and with one or more of a plurality of geographically-distributed metagenomics sequencing centers via one or more networks;

processing metagenomics sequencing results from the plurality of metagenomics sequencing centers associated with respective data zones in the first processing node, wherein the metagenomics sequencing results are obtained in a decentralized and privacy-preserving manner;

configuring a data model based at least in part on the metagenomics sequencing results, wherein the data model comprises a graph in which nodes correspond to respective profiles and edges between the nodes denote relationships between the corresponding profiles;

performing one or more reasoning operations over the data model to infer relationships between entities of the data model that are not directly expressed by the data model, wherein performing one or more reasoning operations over the data model comprises predicting one or more characteristics for an actual or potential outbreak of at least one of a disease, an infection and a contamination;

updating the data model based at least in part on the inferred relationships; and

repeating the processing, configuring, performing and updating for additional metagenomics sequencing results from one or more of the metagenomics sequencing centers;

wherein the method is implemented by at least one processing device comprising a processor coupled to a memory.

2. The method of claim 1 wherein one or more portions of the data model comprise respective profiles each characterizing at least one of a disease, infection or contamination based at least in part on the metagenomics sequencing results.

3. The method of claim 2 wherein a given one of the profiles characterizing at least one of a disease, an infection and a contamination comprises a characterization of said disease, infection or contamination as involving genomic material from multiple ones of a plurality of biological samples sequenced in different ones of the data zones by corresponding different ones of the metagenomics sequencing centers.

4. The method of claim 2 wherein the data model further comprises a plurality of additional categories of profiles including one or more of sample profiles, ecogenome profiles and patient profiles.

5. The method of claim 1 wherein updating the data model based at least in part on the inferred relationships comprises at least one of:

updating a node corresponding to one of the profiles;

adding a node corresponding to a new profile;

deleting a node corresponding to an existing profile; and

inserting one or more additional edges between respective pairs of the profiles of the graph.

6. The method of claim 1 wherein performing one or more reasoning operations over the data model comprises applying a graph traversal tool to the graph in conjunction with performance of the one or more reasoning operations.

7. The method of claim 1 wherein the data model comprises global view information characterizing epidemiological relationships between the metagenomics sequencing results from the metagenomics sequencing centers.

8. The method of claim 7 wherein the global view information comprises a combination of multiple instances of local view information associated with respective ones of the metagenomics sequencing centers.

9. The method of claim 1 wherein the method is implemented in a multi-cluster distributed data processing platform in which each of the metagenomics sequencing centers is associated with a corresponding YARN cluster.

10. The method of claim 9 wherein each of at least a subset of the processing nodes comprises at least one worldwide data node configured to perform operations in accordance with at least one supported framework of a YARN cluster on one or more corresponding portions of the metagenomics sequencing results.

11. The method of claim 1 wherein predicting one or more characteristics comprises predicting a spread pattern for an actual or potential outbreak of at least one of a disease, an infection and a contamination.

12. A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes said at least one processing device:

to configure a first processing node for communication with one or more additional processing nodes and with one or more of a plurality of geographically-distributed metagenomics sequencing centers via one or more networks;

to process metagenomics sequencing results from the plurality of metagenomics sequencing centers associated with respective data zones in the first processing node, wherein the metagenomics sequencing results are obtained in a decentralized and privacy-preserving manner;

to configure a data model based at least in part on the metagenomics sequencing results, wherein the data model comprises a graph in which nodes correspond to respective profiles and edges between the nodes denote relationships between the corresponding profiles;

to perform one or more reasoning operations over the data model to infer relationships between entities of the data model that are not directly expressed by the data model, wherein performing one or more reasoning operations over the data model comprises predicting one or more characteristics for an actual or potential outbreak of at least one of a disease, an infection and a contamination;

to update the data model based at least in part on the inferred relationships; and

to repeat the processing, configuring, performing and updating for additional metagenomics sequencing results from one or more of the metagenomics sequencing centers.

13. The computer program product of claim 12 wherein one or more portions of the data model comprise respective profiles each characterizing at least one of a disease, infection or contamination based at least in part on the metagenomics sequencing results.

14. The computer program product of claim 13 wherein a given one of the profiles characterizing at least one of a disease, an infection and a contamination comprises a characterization of said disease, infection or contamination as involving genomic material from multiple ones of a plurality of biological samples sequenced in different ones of the data zones by corresponding different ones of the metagenomics sequencing centers.

15. The computer program product of claim 12 wherein the data model comprises global view information characterizing epidemiological relationships between the metagenomics sequencing results from the metagenomics sequencing centers.

16. An apparatus comprising:

at least one processing device having a processor coupled to a memory;

wherein said at least one processing device is configured;

to configure a first processing node for communication with one or more additional processing nodes and with one or more of a plurality of geographically-distributed metagenomics sequencing centers via one or more networks;

to process metagenomics sequencing results from the plurality of metagenomics sequencing centers associated with respective data zones in the first processing node, wherein the metagenomics sequencing results are obtained in a decentralized and privacy-preserving manner;

to configure a data model based at least in part on the metagenomics sequencing results, wherein the data model comprises a graph in which nodes correspond to respective profiles and edges between the nodes denote relationships between the corresponding profiles;

to perform one or more reasoning operations over the data model to infer relationships between entities of the data model that are not directly expressed by the data model, wherein performing one or more reasoning operations over the data model comprises predicting one or more characteristics for an actual or potential outbreak of at least one of a disease, an infection and a contamination;

to update the data model based at least in part on the inferred relationships; and

to repeat the processing, configuring, performing and updating for additional metagenomics sequencing results from one or more of the metagenomics sequencing centers.

17. The apparatus of claim 16 wherein one or more portions of the data model comprise respective profiles each characterizing at least one of a disease, infection or contamination based at least in part on the metagenomics sequencing results.

18. The apparatus of claim 17 wherein a given one of the profiles characterizing at least one of a disease, an infection and a contamination comprises a characterization of said disease, infection or contamination as involving genomic material from multiple ones of a plurality of biological samples sequenced in different ones of the data zones by corresponding different ones of the metagenomics sequencing centers.

19. A metagenomics-based biological surveillance system comprising the apparatus of claim 16.

20. The apparatus of claim 16 wherein the data model comprises global view information characterizing epidemiological relationships between the metagenomics sequencing results from the metagenomics sequencing centers.