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biobricks-okg's Introduction

biobricks-okg

An open knowledge graph for BioBricks.ai

Diagrams

UML diagram of mapping 0 generated from okg-ml.yaml UML diagram of mapping 1 generated from okg-ml.yaml UML diagram of mapping 2 generated from okg-ml.yaml

biobricks-okg's People

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john-shaffer avatar tomlue avatar zmughal avatar

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biobricks-okg's Issues

A5 - Data Governance Source Registration Protocol Definition

From the grant

In coordination with Theme 2, BioBricks-OKG will be built so that all data can be accessed using FAIR tools like SPARQL...In this deliverable a checklist will be designed for the integration of new data sources and the management of governance of every integrated source. Other proto-okn groups will be contacted to discover other best practices for source governance.

  1. Launch a knowledge graph (we need to talk to theme 2, but a SPARQL/Gremlin consistent graph would make sense like JENA)
  2. Design a checklist for new local graphs
  3. Create some kind of build tool or quality control tool for confirming that graphs are consistent with checklist.

A1 - Core Brick Creation

From the grant

In this deliverable a set of 6+ assets will be chosen to transform into local, isolated, knowledge graphs. Eventually every compatible BioBricks-AI asset will be added to the knowledge graph.

Let's go a step farther and actually create those local knowledge graphs.

  • Tox21
  • ToxCast
  • HGNC @john-shaffer
  • MESH
  • ICE
  • build Uniprot-kg and integrate

NSF Alpha Report

  • January 17th at 2pm
  • 15-minute presentations

Please prepare a 15-minute presentation covering the following:

  1. Project Overview
  2. Current Project Status
  3. Definition of Alpha-Level Deliverables
  4. Timeline & Schedule: Comparison of planned vs. actual progress, any deviations from the original schedule, updated project timeline, if necessary.
  5. Stakeholder Communication: Summary of communications with key partner agencies and end-users, feedback received, actions taken, and plans for future stakeholder engagement.
  6. Risks & Issues: New risks identified and their potential impact, mitigation strategies implemented/planned.
  7. Upcoming Activities: Critical path going forward, and activities planned for the next quarter.
    After each presentation, there will be a 5-minute Q&A session, followed by a 20โ€“30-minute group discussion at the end on how the three projects plan to collaborate going forward, and how they plan to collaborate with the Theme 2 and Theme 3 projects.

Agency representatives as well as representatives from Themes 2 and 3 projects may be present during these review meetings.

Developmental toxicity of substance X

  • Use case 1:
    1. I want to know developmental toxicity for X.
    2. I want similar compounds.
    3. How do I define similarity?
      a. From the literature, what are the important targets?
      b. What datasets have those targets?
      c. Those assays determine comparators.

Mapping of Modes of Action

  • Use case 2:
    • Automate the process for modes of action.
    • Mapping from data to modes of action and disease mechanism, e.g., Hepatotoxicity
      • Need groupings of mechanisms using the literature.
      • What are the main biological mechanisms that lead to liver toxicity
        (molecular).

Lower priority than #14.

A4 - Source Maintenance Infrastructure Selection

From the grant

... A centralized testing and logging system like DataDog will be integrated into scripts that transform source data into knowledge graphs. Because the BioBricks system already stores versioned snapshots of each data asset, it is possible to maintain a stream of data asset updates and to revert to an older version when there is an error until a fix can be implemented. Insilica developers will be deployed to address these issues as they occur.

Our local knowledge graph creation process should:

  1. allow updates.
  2. have a logging system that allows us to track errors.
  3. Allow reversion to the last successful iteration on failure.

A3 - Ontology Alignment Method Selection and Testing

From the grant

Ontology alignment methods will be reviewed, proposed, and preliminary testing completed to automate the recognition of equivalent classes between knowledge graphs and synchronize them to the core graph.

It is a good idea to start exploring these before implementing too many of the local knowledge graphs in #1 as harmonization methods may inform ontology creation #2 and local knowledge graph constraints #1

getting graph names

https://github.com/biobricks-ai/biobricks-okg/blob/main/report/2024-01-15/graph-names.rq

This query gives me an error.

Virtuoso 37000 Error SP031: SPARQL compiler: The list of return values contains '*' but the pattern does not contain variables

SPARQL query:
define sql:big-data-const 0
#output-format:text/html
define sql:signal-void-variables 1
SELECT DISTINCT ?g_prefix
WHERE {
GRAPH ?g { }
BIND( URI(STRBEFORE(STR(?g), '?file=')) AS ?g_prefix )
}
ORDER BY ?g_prefix

If I update the query to the below I get a timeout. Is there no efficient way to get the graph names in our graph?

Virtuoso 42000 Error The estimated execution time 9360 (sec) exceeds the limit of 400 (sec).

SPARQL query:
SELECT DISTINCT ?g_prefix
WHERE {
GRAPH ?g { ?s ?p ?o }
BIND( URI(STRBEFORE(STR(?g), '?file=')) AS ?g_prefix )
}
ORDER BY ?g_prefix

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