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Article ; Online: Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science.

Unni, Deepak R / Moxon, Sierra A T / Bada, Michael / Brush, Matthew / Bruskiewich, Richard / Caufield, J Harry / Clemons, Paul A / Dancik, Vlado / Dumontier, Michel / Fecho, Karamarie / Glusman, Gustavo / Hadlock, Jennifer J / Harris, Nomi L / Joshi, Arpita / Putman, Tim / Qin, Guangrong / Ramsey, Stephen A / Shefchek, Kent A / Solbrig, Harold /

Soman, Karthik / Thessen, Anne E / Haendel, Melissa A / Bizon, Chris / Mungall, Christopher J

Clinical and translational science

2022 Volume 15, Issue 8, Page(s) 1848–1855

Abstract: Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to ... ...

Abstract	Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.
MeSH term(s)	Knowledge ; Pattern Recognition, Automated ; Translational Science, Biomedical
Language	English
Publishing date	2022-06-06
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	2433157-0
ISSN	1752-8062 ; 1752-8054
ISSN (online)	1752-8062
ISSN	1752-8054
DOI	10.1111/cts.13302
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: KG-Hub-building and exchanging biological knowledge graphs.

Caufield, J Harry / Putman, Tim / Schaper, Kevin / Unni, Deepak R / Hegde, Harshad / Callahan, Tiffany J / Cappelletti, Luca / Moxon, Sierra A T / Ravanmehr, Vida / Carbon, Seth / Chan, Lauren E / Cortes, Katherina / Shefchek, Kent A / Elsarboukh, Glass / Balhoff, Jim / Fontana, Tommaso / Matentzoglu, Nicolas / Bruskiewich, Richard M / Thessen, Anne E /

Harris, Nomi L / Munoz-Torres, Monica C / Haendel, Melissa A / Robinson, Peter N / Joachimiak, Marcin P / Mungall, Christopher J / Reese, Justin T

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 7

Abstract: Motivation: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is ... ...

Abstract	Motivation: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. Results: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification. Availability and implementation: https://kghub.org.
MeSH term(s)	Humans ; Pattern Recognition, Automated ; COVID-19 ; Biological Ontologies ; Rare Diseases ; Machine Learning
Language	English
Publishing date	2023-06-30
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad418
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Underrepresentation of Phenotypic Variability of 16p13.11 Microduplication Syndrome Assessed With an Online Self-Phenotyping Tool (Phenotypr): Cohort Study.

Li, Jianqiao / Hojlo, Margaret A / Chennuri, Sampath / Gujral, Nitin / Paterson, Heather L / Shefchek, Kent A / Genetti, Casie A / Cohn, Emily L / Sewalk, Kara C / Garvey, Emily A / Buttermore, Elizabeth D / Anderson, Nickesha C / Beggs, Alan H / Agrawal, Pankaj B / Brownstein, John S / Haendel, Melissa A / Holm, Ingrid A / Gonzalez-Heydrich, Joseph / Brownstein, Catherine A

Journal of medical Internet research

2021 Volume 23, Issue 3, Page(s) e21023

Abstract: Background: 16p13.11 microduplication syndrome has a variable presentation and is characterized primarily by neurodevelopmental and physical phenotypes resulting from copy number variation at chromosome 16p13.11. Given its variability, there may be ... ...

Abstract	Background: 16p13.11 microduplication syndrome has a variable presentation and is characterized primarily by neurodevelopmental and physical phenotypes resulting from copy number variation at chromosome 16p13.11. Given its variability, there may be features that have not yet been reported. The goal of this study was to use a patient "self-phenotyping" survey to collect data directly from patients to further characterize the phenotypes of 16p13.11 microduplication syndrome. Objective: This study aimed to (1) discover self-identified phenotypes in 16p13.11 microduplication syndrome that have been underrepresented in the scientific literature and (2) demonstrate that self-phenotyping tools are valuable sources of data for the medical and scientific communities. Methods: As part of a large study to compare and evaluate patient self-phenotyping surveys, an online survey tool, Phenotypr, was developed for patients with rare disorders to self-report phenotypes. Participants with 16p13.11 microduplication syndrome were recruited through the Boston Children's Hospital 16p13.11 Registry. Either the caregiver, parent, or legal guardian of an affected child or the affected person (if aged 18 years or above) completed the survey. Results were securely transferred to a Research Electronic Data Capture database and aggregated for analysis. Results: A total of 19 participants enrolled in the study. Notably, among the 19 participants, aggression and anxiety were mentioned by 3 (16%) and 4 (21%) participants, respectively, which is an increase over the numbers in previously published literature. Additionally, among the 19 participants, 3 (16%) had asthma and 2 (11%) had other immunological disorders, both of which have not been previously described in the syndrome. Conclusions: Several phenotypes might be underrepresented in the previous 16p13.11 microduplication literature, and new possible phenotypes have been identified. Whenever possible, patients should continue to be referenced as a source of complete phenotyping data on their condition. Self-phenotyping may lead to a better understanding of the prevalence of phenotypes in genetic disorders and may identify previously unreported phenotypes.
MeSH term(s)	Biological Variation, Population ; Cohort Studies ; DNA Copy Number Variations ; Family ; Humans ; Phenotype
Language	English
Publishing date	2021-03-16
Publishing country	Canada
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/21023
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Biolink Model

Unni, Deepak R. / Moxon, Sierra A. T. / Bada, Michael / Brush, Matthew / Bruskiewich, Richard / Clemons, Paul / Dancik, Vlado / Dumontier, Michel / Fecho, Karamarie / Glusman, Gustavo / Hadlock, Jennifer J. / Harris, Nomi L. / Joshi, Arpita / Putman, Tim / Qin, Guangrong / Ramsey, Stephen A. / Shefchek, Kent A. / Solbrig, Harold / Soman, Karthik /

Thessen, Anne T. / Haendel, Melissa A. / Bizon, Chris / Mungall, Christopher J. / Consortium, the Biomedical Data Translator

A Universal Schema for Knowledge Graphs in Clinical, Biomedical, and Translational Science

2022

Abstract: Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness between core biomedical concepts, enable data structures ... ...

Abstract	Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness between core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally-accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates), representing biomedical entities such as gene, disease, chemical, anatomical structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.
Keywords	Computer Science - Databases
Subject code	400
Publishing date	2022-03-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.

Reese, Justin T / Unni, Deepak / Callahan, Tiffany J / Cappelletti, Luca / Ravanmehr, Vida / Carbon, Seth / Shefchek, Kent A / Good, Benjamin M / Balhoff, James P / Fontana, Tommaso / Blau, Hannah / Matentzoglu, Nicolas / Harris, Nomi L / Munoz-Torres, Monica C / Haendel, Melissa A / Robinson, Peter N / Joachimiak, Marcin P / Mungall, Christopher J

Patterns (New York, N.Y.)

2020 Volume 2, Issue 1, Page(s) 100155

Abstract: Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), ... ...

Abstract	Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.
Keywords	covid19
Language	English
Publishing date	2020-11-09
Publishing country	United States
Document type	Journal Article
ISSN	2666-3899
ISSN (online)	2666-3899
DOI	10.1016/j.patter.2020.100155
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Putman, Tim E / Schaper, Kevin / Matentzoglu, Nicolas / Rubinetti, Vincent P / Alquaddoomi, Faisal S / Cox, Corey / Caufield, J Harry / Elsarboukh, Glass / Gehrke, Sarah / Hegde, Harshad / Reese, Justin T / Braun, Ian / Bruskiewich, Richard M / Cappelletti, Luca / Carbon, Seth / Caron, Anita R / Chan, Lauren E / Chute, Christopher G / Cortes, Katherina G /

De Souza, Vinícius / Fontana, Tommaso / Harris, Nomi L / Hartley, Emily L / Hurwitz, Eric / Jacobsen, Julius O B / Krishnamurthy, Madan / Laraway, Bryan J / McLaughlin, James A / McMurry, Julie A / Moxon, Sierra A T / Mullen, Kathleen R / O'Neil, Shawn T / Shefchek, Kent A / Stefancsik, Ray / Toro, Sabrina / Vasilevsky, Nicole A / Walls, Ramona L / Whetzel, Patricia L / Osumi-Sutherland, David / Smedley, Damian / Robinson, Peter N / Mungall, Christopher J / Haendel, Melissa A / Munoz-Torres, Monica C

Nucleic acids research

2023 Volume 52, Issue D1, Page(s) D938–D949

Abstract: Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch ... ...

Abstract	Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.
MeSH term(s)	Humans ; Phenotype ; Internet ; Databases, Factual/standards ; Software ; Genes/genetics ; Disease/genetics
Language	English
Publishing date	2023-11-22
Publishing country	England
Document type	Journal Article
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkad1082
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: KG-Hub -- Building and Exchanging Biological Knowledge Graphs

Caufield, J Harry / Putman, Tim / Schaper, Kevin / Unni, Deepak R / Hegde, Harshad / Callahan, Tiffany J / Cappelletti, Luca / Moxon, Sierra AT / Ravanmehr, Vida / Carbon, Seth / Chan, Lauren E / Cortes, Katherina / Shefchek, Kent A / Elsarboukh, Glass / Balhoff, James P / Fontana, Tommaso / Matentzoglu, Nicolas / Bruskiewich, Richard M / Thessen, Anne E /

Harris, Nomi L / Munoz-Torres, Monica C / Haendel, Melissa A / Robinson, Peter N / Joachimiak, Marcin P / Mungall, Christopher J / Reese, Justin T

2023

Abstract: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is ... ...

Abstract	Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of knowledge graphs. Features include a simple, modular extract-transform-load (ETL) pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate knowledge graphs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph machine learning, including node embeddings and training of models for link prediction and node classification.
Keywords	Quantitative Biology - Quantitative Methods ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	004
Publishing date	2023-01-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Underrepresentation of Phenotypic Variability of 16p13.11 Microduplication Syndrome Assessed With an Online Self-Phenotyping Tool (Phenotypr)

Journal of Medical Internet Research, Vol 23, Iss 3, p e

Cohort Study

2021 Volume 21023

Abstract: Background16p13.11 microduplication syndrome has a variable presentation and is characterized primarily by neurodevelopmental and physical phenotypes resulting from copy number variation at chromosome 16p13.11. Given its variability, there may be ... ...

Abstract	Background16p13.11 microduplication syndrome has a variable presentation and is characterized primarily by neurodevelopmental and physical phenotypes resulting from copy number variation at chromosome 16p13.11. Given its variability, there may be features that have not yet been reported. The goal of this study was to use a patient “self-phenotyping” survey to collect data directly from patients to further characterize the phenotypes of 16p13.11 microduplication syndrome. ObjectiveThis study aimed to (1) discover self-identified phenotypes in 16p13.11 microduplication syndrome that have been underrepresented in the scientific literature and (2) demonstrate that self-phenotyping tools are valuable sources of data for the medical and scientific communities. MethodsAs part of a large study to compare and evaluate patient self-phenotyping surveys, an online survey tool, Phenotypr, was developed for patients with rare disorders to self-report phenotypes. Participants with 16p13.11 microduplication syndrome were recruited through the Boston Children's Hospital 16p13.11 Registry. Either the caregiver, parent, or legal guardian of an affected child or the affected person (if aged 18 years or above) completed the survey. Results were securely transferred to a Research Electronic Data Capture database and aggregated for analysis. ResultsA total of 19 participants enrolled in the study. Notably, among the 19 participants, aggression and anxiety were mentioned by 3 (16%) and 4 (21%) participants, respectively, which is an increase over the numbers in previously published literature. Additionally, among the 19 participants, 3 (16%) had asthma and 2 (11%) had other immunological disorders, both of which have not been previously described in the syndrome. ConclusionsSeveral phenotypes might be underrepresented in the previous 16p13.11 microduplication literature, and new possible phenotypes have been identified. Whenever possible, patients should continue to be referenced as a source of complete phenotyping data on their ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Public aspects of medicine ; RA1-1270
Subject code	150
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

Patterns (N Y)

Abstract	Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks-the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #917402
Database	COVID19

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Article ; Online: KG-COVID-19

Reese, Justin T. / Unni, Deepak / Callahan, Tiffany J. / Cappelletti, Luca / Ravanmehr, Vida / Carbon, Seth / Shefchek, Kent A. / Good, Benjamin M. / Balhoff, James P. / Fontana, Tommaso / Blau, Hannah / Matentzoglu, Nicolas / Harris, Nomi L. / Munoz-Torres, Monica C. / Haendel, Melissa A. / Robinson, Peter N. / Joachimiak, Marcin P. / Mungall, Christopher J.

Patterns

a framework to produce customized knowledge graphs for COVID-19 response

2020 , Page(s) 100155

Keywords	covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ISSN	2666-3899
DOI	10.1016/j.patter.2020.100155
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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