Why we need all the organisms: an exploration of the Monarch knowledge graph to aid mechanism discovery
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908552715567104 |
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| author | Cortes, Katherina Korn, Daniel Gehrke, Sarah Schaper, Kevin Cox, Corey Golden, Patrick Odell, Aaron Laraway, Bryan Krishnamurthy, Madan Reese, Justin Caufield, Harry Moxon, Sierra Elias, Ellen Mungall, Christopher J Haendel, Melissa |
| author_facet | Cortes, Katherina Korn, Daniel Gehrke, Sarah Schaper, Kevin Cox, Corey Golden, Patrick Odell, Aaron Laraway, Bryan Krishnamurthy, Madan Reese, Justin Caufield, Harry Moxon, Sierra Elias, Ellen Mungall, Christopher J Haendel, Melissa |
| contents | Research done using model organisms has been fundamental to the biological understanding of human genes, diseases and phenotypes. Model organisms provide tractable systems for experiments to enhance understanding of biological mechanisms conserved across the evolutionary tree. Decades of model organism research has generated vast amounts of data; however, this data is split across many domains, organisms, and biological fields of research. Knowledge graphs (KGs) are a computational way to aggregate and compile disparate information in a parsable format. By unifying data across studies, organisms and time points, KG researchers can create novel targeted hypotheses. Here we demonstrate how model organisms are connected to humans and other organisms through genes, diseases and phenotypes allowing for a broader understanding of genetic biology than just one organism alone can provide. Utilizing resources such as the Monarch KG is a great way to reduce redundant experiments and find directions previously unexplored. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18050 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Why we need all the organisms: an exploration of the Monarch knowledge graph to aid mechanism discovery Cortes, Katherina Korn, Daniel Gehrke, Sarah Schaper, Kevin Cox, Corey Golden, Patrick Odell, Aaron Laraway, Bryan Krishnamurthy, Madan Reese, Justin Caufield, Harry Moxon, Sierra Elias, Ellen Mungall, Christopher J Haendel, Melissa Quantitative Methods Research done using model organisms has been fundamental to the biological understanding of human genes, diseases and phenotypes. Model organisms provide tractable systems for experiments to enhance understanding of biological mechanisms conserved across the evolutionary tree. Decades of model organism research has generated vast amounts of data; however, this data is split across many domains, organisms, and biological fields of research. Knowledge graphs (KGs) are a computational way to aggregate and compile disparate information in a parsable format. By unifying data across studies, organisms and time points, KG researchers can create novel targeted hypotheses. Here we demonstrate how model organisms are connected to humans and other organisms through genes, diseases and phenotypes allowing for a broader understanding of genetic biology than just one organism alone can provide. Utilizing resources such as the Monarch KG is a great way to reduce redundant experiments and find directions previously unexplored. |
| title | Why we need all the organisms: an exploration of the Monarch knowledge graph to aid mechanism discovery |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2509.18050 |