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Article: Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

Chan, Lauren E / Casiraghi, Elena / Putman, Tim / Reese, Justin / Harmon, Quaker E / Schaper, Kevin / Hedge, Harshad / Valentini, Giorgio / Schmitt, Charles / Motsinger-Reif, Alison / Hall, Janet E / Mungall, Christopher J / Robinson, Peter N / Haendel, Melissa A

medRxiv : the preprint server for health sciences

2023

Abstract: Objective: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors ... ...

Abstract	Objective: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (e.g., endometriosis, ovarian cyst, and uterine fibroids). Materials and methods: We harmonized survey data from the Personalized Environment and Genes Study on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. Results: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. Discussion: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal, but can support hypothesis generation. Conclusion: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.
Language	English
Publishing date	2023-07-16
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.07.14.23292679
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: ChlamBase: a curated model organism database for the Chlamydia research community.

Putman, Tim / Hybiske, Kevin / Jow, Derek / Afrasiabi, Cyrus / Lelong, Sebastien / Cano, Marco Alvarado / Wu, Chunlei / Su, Andrew I

Database : the journal of biological databases and curation

2019 Volume 2019

Abstract: The accelerating growth of genomic and proteomic information for Chlamydia species, coupled with unique biological aspects of these pathogens, necessitates bioinformatic tools and features that are not provided by major public databases. To meet these ... ...

Abstract	The accelerating growth of genomic and proteomic information for Chlamydia species, coupled with unique biological aspects of these pathogens, necessitates bioinformatic tools and features that are not provided by major public databases. To meet these growing needs, we developed ChlamBase, a model organism database for Chlamydia that is built upon the WikiGenomes application framework, and Wikidata, a community-curated database. ChlamBase was designed to serve as a central access point for genomic and proteomic information for the Chlamydia research community. ChlamBase integrates information from numerous external databases, as well as important data extracted from the literature that are otherwise not available in structured formats that are easy to use. In addition, a key feature of ChlamBase is that it empowers users in the field to contribute new annotations and data as the field advances with continued discoveries. ChlamBase is freely and publicly available at chlambase.org.
MeSH term(s)	Chlamydia/classification ; Chlamydia/genetics ; Chlamydia/metabolism ; Data Curation ; Databases, Genetic ; Genomics ; Proteomics
Language	English
Publishing date	2019-02-04
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2496706-3
ISSN	1758-0463 ; 1758-0463
ISSN (online)	1758-0463
ISSN	1758-0463
DOI	10.1093/database/baz041
Database	MEDical Literature Analysis and Retrieval System OnLINE

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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
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Article: The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences.

Stefancsik, Ray / Balhoff, James P / Balk, Meghan A / Ball, Robyn / Bello, Susan M / Caron, Anita R / Chessler, Elissa / de Souza, Vinicius / Gehrke, Sarah / Haendel, Melissa / Harris, Laura W / Harris, Nomi L / Ibrahim, Arwa / Koehler, Sebastian / Matentzoglu, Nicolas / McMurry, Julie A / Mungall, Christopher J / Munoz-Torres, Monica C / Putman, Tim /

Robinson, Peter / Smedley, Damian / Sollis, Elliot / Thessen, Anne E / Vasilevsky, Nicole / Walton, David O / Osumi-Sutherland, David

bioRxiv : the preprint server for biology

2023

Abstract: Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the ... ...

Abstract	Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
Language	English
Publishing date	2023-01-27
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.01.26.525742
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
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Article ; Online: The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.

Stefancsik, Ray / Balhoff, James P / Balk, Meghan A / Ball, Robyn L / Bello, Susan M / Caron, Anita R / Chesler, Elissa J / de Souza, Vinicius / Gehrke, Sarah / Haendel, Melissa / Harris, Laura W / Harris, Nomi L / Ibrahim, Arwa / Koehler, Sebastian / Matentzoglu, Nicolas / McMurry, Julie A / Mungall, Christopher J / Munoz-Torres, Monica C / Putman, Tim /

Robinson, Peter / Smedley, Damian / Sollis, Elliot / Thessen, Anne E / Vasilevsky, Nicole / Walton, David O / Osumi-Sutherland, David

Mammalian genome : official journal of the International Mammalian Genome Society

2023 Volume 34, Issue 3, Page(s) 364–378

Abstract	Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
MeSH term(s)	Genome-Wide Association Study ; Biological Science Disciplines ; Phenotype ; Biological Ontologies
Language	English
Publishing date	2023-04-19
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1058547-3
ISSN	1432-1777 ; 0938-8990
ISSN (online)	1432-1777
ISSN	0938-8990
DOI	10.1007/s00335-023-09992-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: ChlamBase: a curated model organism database for the Chlamydia research community.

Putman, Tim / Hybiske, Kevin / Jow, Derek / Afrasiabi, Cyrus / Lelong, Sebastien / Cano, Marco Alvarado / Stupp, Gregory S / Waagmeester, Andra / Good, Benjamin M / Wu, Chunlei / Su, Andrew I

Database : the journal of biological databases and curation

2019 Volume 2019

Language	English
Publishing date	2019-05-14
Publishing country	England
Document type	Journal Article ; Published Erratum
ZDB-ID	2496706-3
ISSN	1758-0463 ; 1758-0463
ISSN (online)	1758-0463
ISSN	1758-0463
DOI	10.1093/database/baz091
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Article ; Online: Centralizing content and distributing labor: a community model for curating the very long tail of microbial genomes.

Putman, Tim E / Burgstaller-Muehlbacher, Sebastian / Waagmeester, Andra / Wu, Chunlei / Su, Andrew I / Good, Benjamin M

Database : the journal of biological databases and curation

2016 Volume 2016

Abstract: The last 20 years of advancement in sequencing technologies have led to sequencing thousands of microbial genomes, creating mountains of genetic data. While efficiency in generating the data improves almost daily, applying meaningful relationships ... ...

Abstract	The last 20 years of advancement in sequencing technologies have led to sequencing thousands of microbial genomes, creating mountains of genetic data. While efficiency in generating the data improves almost daily, applying meaningful relationships between taxonomic and genetic entities on this scale requires a structured and integrative approach. Currently, knowledge is distributed across a fragmented landscape of resources from government-funded institutions such as National Center for Biotechnology Information (NCBI) and UniProt to topic-focused databases like the ODB3 database of prokaryotic operons, to the supplemental table of a primary publication. A major drawback to large scale, expert-curated databases is the expense of maintaining and extending them over time. No entity apart from a major institution with stable long-term funding can consider this, and their scope is limited considering the magnitude of microbial data being generated daily. Wikidata is an openly editable, semantic web compatible framework for knowledge representation. It is a project of the Wikimedia Foundation and offers knowledge integration capabilities ideally suited to the challenge of representing the exploding body of information about microbial genomics. We are developing a microbial specific data model, based on Wikidata's semantic web compatibility, which represents bacterial species, strains and the gene and gene products that define them. Currently, we have loaded 43,694 gene and 37,966 protein items for 21 species of bacteria, including the human pathogenic bacteriaChlamydia trachomatis.Using this pathogen as an example, we explore complex interactions between the pathogen, its host, associated genes, other microbes, disease and drugs using the Wikidata SPARQL endpoint. In our next phase of development, we will add another 99 bacterial genomes and their gene and gene products, totaling ∼900,000 additional entities. This aggregation of knowledge will be a platform for community-driven collaboration, allowing the networking of microbial genetic data through the sharing of knowledge by both the data and domain expert.
MeSH term(s)	Data Curation ; Female ; Gene Ontology ; Genes, Bacterial ; Genome, Microbial ; Humans ; Models, Theoretical ; Molecular Sequence Annotation ; Operon/genetics ; Search Engine
Language	English
Publishing date	2016
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2496706-3
ISSN	1758-0463 ; 1758-0463
ISSN (online)	1758-0463
ISSN	1758-0463
DOI	10.1093/database/baw028
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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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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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