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Santangelo BE, Apgar M, Colorado ASB, Martin CG, Sterrett J, Wall E, Joachimiak MP, Hunter LE, Lozupone CA. Integrating biological knowledge for mechanistic inference in the host-associated microbiome. Front Microbiol 2024; 15:1351678. [PMID: 38638909 PMCID: PMC11024261 DOI: 10.3389/fmicb.2024.1351678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/26/2024] [Indexed: 04/20/2024] Open
Abstract
Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.
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Affiliation(s)
- Brook E. Santangelo
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Madison Apgar
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | | | - Casey G. Martin
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - John Sterrett
- Department of Integrative Physiology, University of Colorado, Boulder, CO, United States
| | - Elena Wall
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Marcin P. Joachimiak
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Biosystems Data Science Department, Berkeley, CA, United States
| | - Lawrence E. Hunter
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Catherine A. Lozupone
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
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Fu C, Huang Z, van Harmelen F, He T, Jiang X. Food4healthKG: Knowledge graphs for food recommendations based on gut microbiota and mental health. Artif Intell Med 2023; 145:102677. [PMID: 37925207 DOI: 10.1016/j.artmed.2023.102677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/05/2023] [Accepted: 10/03/2023] [Indexed: 11/06/2023]
Abstract
Food is increasingly acknowledged as a powerful means to promote and maintain mental health. The introduction of the gut-brain axis has been instrumental in understanding the impact of food on mental health. It is widely reported that food can significantly influence gut microbiota metabolism, thereby playing a pivotal role in maintaining mental health. However, the vast amount of heterogeneous data published in recent research lacks systematic integration and application development. To remedy this, we construct a comprehensive knowledge graph, named Food4healthKG, focusing on food, gut microbiota, and mental diseases. The constructed workflow includes the integration of numerous heterogeneous data, entity linking to a normalized format, and the well-designed representation of the acquired knowledge. To illustrate the availability of Food4healthKG, we design two case studies: the knowledge query and the food recommendation based on Food4healthKG. Furthermore, we propose two evaluation methods to validate the quality of the results obtained from Food4healthKG. The results demonstrate the system's effectiveness in practical applications, particularly in providing convincing food recommendations based on gut microbiota and mental health. Food4healthKG is accessible at https://github.com/ccszbd/Food4healthKG.
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Affiliation(s)
- Chengcheng Fu
- National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China; School of Computer Science, Central China Normal University, Wuhan, China; Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; National Language Resources Monitor Research Center for Network Media, Central China Normal University, Wuhan, China
| | - Zhisheng Huang
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China; Deep Blue Technology Group, Shanghai, China
| | - Frank van Harmelen
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tingting He
- School of Computer Science, Central China Normal University, Wuhan, China; National Language Resources Monitor Research Center for Network Media, Central China Normal University, Wuhan, China
| | - Xingpeng Jiang
- School of Computer Science, Central China Normal University, Wuhan, China; National Language Resources Monitor Research Center for Network Media, Central China Normal University, Wuhan, China.
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Dang LD, Phan UTP, Nguyen NTH. GENA: A knowledge graph for nutrition and mental health. J Biomed Inform 2023; 145:104460. [PMID: 37532000 DOI: 10.1016/j.jbi.2023.104460] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 07/23/2023] [Accepted: 07/24/2023] [Indexed: 08/04/2023]
Abstract
While a large number of knowledge graphs have previously been developed by automatically extracting and structuring knowledge from literature, there is currently no such knowledge graph that encodes relationships between food, biochemicals and mental illnesses, even though a large amount of knowledge about these relationships is available in the form of unstructured text in biomedical literature articles. To address this limitation, this article describes the development of GENA - (Graph of mEntal-health and Nutrition Association), a knowledge graph that represents relations between nutrition and mental health, extracted from biomedical abstracts. GENA is constructed from PubMed abstracts that contain keywords relating to chemicals, food, and health. A hybrid named entity recognition (NER) model is firstly applied to these abstracts to identify various entities of interest. Subsequently, a deep syntax-based relation extraction model is used to detect binary relations between the identified entities. Finally, the resulting relations are used to populate the GENA knowledge graph, whose relationships can be accessed in an intuitive and interpretable manner using the Neo4J Database Management System. To evaluate the reliability of GENA, two annotators manually assessed a subset of the extracted relations. The evaluation results show that our methods obtain high precision for the NER task and acceptable precision and relative recall for the relation extraction task. GENA consists of 43,367 relationships that encode information about nutrition and health, of which 94.04% are new relations that are not present in existing ontologies of food and diseases. GENA is constructed based on scientific principles, and has the potential to be used within further applications to contribute towards scientific research within the domain. It is a pioneering knowledge graph in nutrition and mental health, containing a diverse range of relationship types. All of our source code and results are publicly available at https://github.com/ddlinh/gena-db.
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Affiliation(s)
- Linh D Dang
- Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam.
| | - Uyen T P Phan
- Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam; Vietnam National University, Ho Chi Minh City, Vietnam
| | - Nhung T H Nguyen
- Department of Computer Science, The University of Manchester, Manchester, United Kingdom
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Liu T, Lan G, Feenstra KA, Huang Z, Heringa J. Towards a knowledge graph for pre-/probiotics and microbiota-gut-brain axis diseases. Sci Rep 2022; 12:18977. [PMID: 36347868 PMCID: PMC9643397 DOI: 10.1038/s41598-022-21735-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 09/30/2022] [Indexed: 11/09/2022] Open
Abstract
Scientific publications present biological relationships but are structured for human reading, making it difficult to use this resource for semantic integration and querying. Existing databases, on the other hand, are well structured for automated analysis, but do not contain comprehensive biological knowledge. We devised an approach for constructing comprehensive knowledge graphs from these two types of resources and applied it to investigate relationships between pre-/probiotics and microbiota-gut-brain axis diseases. To this end, we created (i) a knowledge base, dubbed ppstatement, containing manually curated detailed annotations, and (ii) a knowledge base, called ppconcept, containing automatically annotated concepts. The resulting Pre-/Probiotics Knowledge Graph (PPKG) combines these two knowledge bases with three other public databases (i.e. MeSH, UMLS and SNOMED CT). To validate the performance of PPKG and to demonstrate the added value of integrating two knowledge bases, we created four biological query cases. The query cases demonstrate that we can retrieve co-occurring concepts of interest, and also that combining the two knowledge bases leads to more comprehensive query results than utilizing them separately. The PPKG enables users to pose research queries such as "which pre-/probiotics combinations may benefit depression?", potentially leading to novel biological insights.
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Affiliation(s)
- Ting Liu
- grid.12380.380000 0004 1754 9227Department of Computer Science, Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands ,grid.12380.380000 0004 1754 9227Knowledge Representation and Reasoning Group, Department of Computer Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Gongjin Lan
- grid.263817.90000 0004 1773 1790Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China
| | - K. Anton Feenstra
- grid.12380.380000 0004 1754 9227Department of Computer Science, Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Zhisheng Huang
- grid.12380.380000 0004 1754 9227Knowledge Representation and Reasoning Group, Department of Computer Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Jaap Heringa
- grid.12380.380000 0004 1754 9227Department of Computer Science, Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
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