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Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans

In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scient...

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Autores principales: Prakash, Sharan J., Van Auken, Kimberly M., Hill, David P., Sternberg, Paul W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168330/
https://www.ncbi.nlm.nih.gov/pubmed/37162850
http://dx.doi.org/10.1101/2023.04.28.538760
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author Prakash, Sharan J.
Van Auken, Kimberly M.
Hill, David P.
Sternberg, Paul W.
author_facet Prakash, Sharan J.
Van Auken, Kimberly M.
Hill, David P.
Sternberg, Paul W.
author_sort Prakash, Sharan J.
collection PubMed
description In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate different types of experimental data from multiple papers and biological knowledge bases in a unifying data model, providing a complementary method to manual review for interacting with published knowledge. The Gene Ontology Consortium (GOC) has created a semantic modelling framework that extends individual functional gene annotations to structured descriptions of causal networks representing biological processes (Gene Ontology Causal Activity Modelling, or GO-CAM). In this study, we explored whether the GO-CAM framework could represent knowledge of the causal relationships between environmental inputs, neural circuits and behavior in the model nematode C. elegans (C. elegans Neural Circuit Causal Activity Modelling (CeN-CAM)). We found that, given extensions to several relevant ontologies, a wide variety of author statements from the literature about the neural circuit basis of egg-laying and carbon dioxide (CO(2)) avoidance behaviors could be faithfully represented with CeN-CAM. Through this process, we were able to generate generic data models for several categories of experimental results. We also discuss how semantic modelling may be used to functionally annotate the C. elegans connectome. Thus, Gene Ontology-based semantic modelling has the potential to support various machine-readable representations of neurobiological knowledge.
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spelling pubmed-101683302023-05-10 Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans Prakash, Sharan J. Van Auken, Kimberly M. Hill, David P. Sternberg, Paul W. bioRxiv Article In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate different types of experimental data from multiple papers and biological knowledge bases in a unifying data model, providing a complementary method to manual review for interacting with published knowledge. The Gene Ontology Consortium (GOC) has created a semantic modelling framework that extends individual functional gene annotations to structured descriptions of causal networks representing biological processes (Gene Ontology Causal Activity Modelling, or GO-CAM). In this study, we explored whether the GO-CAM framework could represent knowledge of the causal relationships between environmental inputs, neural circuits and behavior in the model nematode C. elegans (C. elegans Neural Circuit Causal Activity Modelling (CeN-CAM)). We found that, given extensions to several relevant ontologies, a wide variety of author statements from the literature about the neural circuit basis of egg-laying and carbon dioxide (CO(2)) avoidance behaviors could be faithfully represented with CeN-CAM. Through this process, we were able to generate generic data models for several categories of experimental results. We also discuss how semantic modelling may be used to functionally annotate the C. elegans connectome. Thus, Gene Ontology-based semantic modelling has the potential to support various machine-readable representations of neurobiological knowledge. Cold Spring Harbor Laboratory 2023-09-26 /pmc/articles/PMC10168330/ /pubmed/37162850 http://dx.doi.org/10.1101/2023.04.28.538760 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Prakash, Sharan J.
Van Auken, Kimberly M.
Hill, David P.
Sternberg, Paul W.
Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans
title Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans
title_full Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans
title_fullStr Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans
title_full_unstemmed Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans
title_short Semantic Representation of Neural Circuit Knowledge in Caenorhabditis elegans
title_sort semantic representation of neural circuit knowledge in caenorhabditis elegans
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168330/
https://www.ncbi.nlm.nih.gov/pubmed/37162850
http://dx.doi.org/10.1101/2023.04.28.538760
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