Constructing knowledge graphs and their biomedical applications.
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ABSTRACT:
SUBMITTER: Nicholson DN
PROVIDER: S-EPMC7327409 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Constructing knowledge graphs and their biomedical applications.
Nicholson David N DN Greene Casey S CS
Computational and structural biotechnology journal 20200602
Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated s ...[more]
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| S-EPMC8277235 | biostudies-literature
Project description:MotivationModern problems of concept annotation associate an object of interest (gene, individual, text document) with a set of interrelated textual descriptors (functions, diseases, topics), often organized in concept hierarchies or ontologies. Most ontology can be seen as directed acyclic graphs (DAGs), where nodes represent concepts and edges represent relational ties between these concepts. Given an ontology graph, each object can only be annotated by a consistent sub-graph; that is, a sub-graph such that if an object is annotated by a particular concept, it must also be annotated by all other concepts that generalize it. Ontologies therefore provide a compact representation of a large space of possible consistent sub-graphs; however, until now we have not been aware of a practical algorithm that can enumerate such annotation spaces for a given ontology.ResultsWe propose an algorithm for enumerating consistent sub-graphs of DAGs. The algorithm recursively partitions the graph into strictly smaller graphs until the resulting graph becomes a rooted tree (forest), for which a linear-time solution is computed. It then combines the tallies from graphs created in the recursion to obtain the final count. We prove the correctness of this algorithm, propose several practical accelerations, evaluate it on random graphs and then apply it to characterize four major biomedical ontologies. We believe this work provides valuable insights into the complexity of concept annotation spaces and its potential influence on the predictability of ontological annotation.Availability and implementationhttps://github.com/shawn-peng/counting-consistent-sub-DAG.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6022688 | biostudies-literature