Exploring the Microbiota-Gut-Brain Axis for Mental Disorders with Knowledge Graphs

Liu, Ting; Pan, Xueli; Wang, Xu; Feenstra, K. Anton; Heringa, Jaap; Huang, Zhisheng

doi:10.2991/jaims.d.201208.001

Cited by 9 publications

(14 citation statements)

References 63 publications

(75 reference statements)

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“…It is unknown whether PD starts in the gut, though integrated understanding of intestinal permeability and the pathological implications of the disease highlight the importance of the gut microbiome in the development of PD. Bacteria can produce neurotransmitters and neuromodulators that may affect the pathogenesis of the disease, and the differences in microbiome signatures have become clearer in healthy vs PD individuals [14]. As shown in Figure 3, there are an abundance of paths between these microbes with those diseases in MGMLink.…”

Section: Resultsmentioning

confidence: 99%

“…However, these applications have yet to be extended into the microbiome field [7]. Existing microbial KGs incorporate information more focused on microbial trait outcomes [13] or are limited in scope and/or lacking in biomedical information [2,14,15]. Here we describe the creation and application of a KG of microbe-host interactions toward the creation of mechanistic hypotheses regarding microbe-disease relationships.…”

Section: Introductionmentioning

confidence: 99%

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Hypothesizing mechanistic links between microbes and disease using knowledge graphs

Santangelo,

Bada,

Hunter

et al. 2023

Preprint

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Knowledge graphs have found broad biomedical applications, providing useful representations of complex knowledge. Although plentiful evidence exists linking the gut microbiome to disease, mechanistic understanding of those relationships remains generally elusive. Here we demonstrate the potential of knowledge graphs to hypothesize plausible mechanistic accounts of host-microbe interactions in disease. To do so, we constructed a knowledge graph of linked microbes, genes and metabolites called MGMLink. Using a semantically constrained shortest path search through the graph and a novel path prioritization methodology based on cosine similarity, we show that this knowledge supports inference of mechanistic hypotheses that explain observed relationships between microbes and disease phenotypes. We discuss specific applications of this methodology in inflammatory bowel disease and Parkinson’s disease. This approach enables mechanistic hypotheses surrounding the complex interactions between gut microbes and disease to be generated in a scalable and comprehensive manner.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hypothesizing mechanistic links between microbes and disease using knowledge graphs

Santangelo,

Bada,

Hunter

et al. 2023

Preprint

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“…Many researchers associate their data with popular databases when building knowledge graphs. For example, in 29 , the authors enriched their knowledge base by linking to databases UMLS, MeSH, and SNOMED CT. The authors save time for constructing the taxonomy of gut microbiota, since these databases contain taxonomic information on microorganisms.…”

Section: Methodology (Ten Criteria)mentioning

confidence: 99%

A semantic web technology index

Lan

Liu

Wang

et al. 2022

Sci Rep

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Semantic web (SW) technology has been widely applied to many domains such as medicine, health care, finance, geology. At present, researchers mainly rely on their experience and preferences to develop and evaluate the work of SW technology. Although the general architecture (e.g., Tim Berners-Lee’s Semantic Web Layer Cake) of SW technology was proposed many years ago and has been well-known, it still lacks a concrete guideline for standardizing the development of SW technology. In this paper, we propose an SW technology index to standardize the development for ensuring that the work of SW technology is designed well and to quantitatively evaluate the quality of the work in SW technology. This index consists of 10 criteria that quantify the quality as a score of $$0{-}10$$ 0 - 10 . We address each criterion in detail for a clear explanation from three aspects: (1) what is the criterion? (2) why do we consider this criterion and (3) how do the current studies meet this criterion? Finally, we present the validation of this index by providing some examples of how to apply the index to the validation cases. We conclude that the index is a useful standard to guide and evaluate the work in SW technology.

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“…Especially for knowledge discovery based on biomedical literatures, knowledge graphs can realize effective organization, rapid query and deep reasoning application of semantic knowledge. For the studies of gut microbiota, a large number of biomedical literature information have been extracted and updated from PubMed, which have undergone a certain degree of automatic processing and stored in a knowledge base to provide several query application [19][20] . Based on this knowledge base, this study is dedicated to discovery relationships between nutritional disorders and gut microbiota.…”

Section: Related Workmentioning

confidence: 99%

“…A large number of literatures related to gut microbiota from PubMed have been extracted and stored in MedicalKG, which is a large knowledge base and constantly updated [19][20] . And at present, MedicalKG has stored 107,417,061 statements.…”

Section: Information Extractionmentioning

confidence: 99%

Relationships Discovery between Nutritional Disorders and Gut Microbiota with Knowledge Graphs

Huang

Chen

et al. 2021

Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences

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Exploring the Microbiota-Gut-Brain Axis for Mental Disorders with Knowledge Graphs

Cited by 9 publications

References 63 publications

Hypothesizing mechanistic links between microbes and disease using knowledge graphs

Hypothesizing mechanistic links between microbes and disease using knowledge graphs

A semantic web technology index

Relationships Discovery between Nutritional Disorders and Gut Microbiota with Knowledge Graphs

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