Predicting the relationships between gut microbiota and mental disorders with knowledge graphs.

Published on Dec 1, 2021
· DOI :10.1007/S13755-020-00128-2
Ting Liu2
Estimated H-index: 2
(VU: VU University Amsterdam),
Xueli Pan2
Estimated H-index: 2
(VU: VU University Amsterdam)
+ 3 AuthorsZhisheng Huang22
Estimated H-index: 22
(Capital Medical University)
Sources
Abstract
Gut microbiota produce and modulate the production of neurotransmitters which have been implicated in mental disorders. Neurotransmitters may act as 'matchmaker' between gut microbiota imbalance and mental disorders. Most of the relevant research effort goes into the relationship between gut microbiota and neurotransmitters and the other between neurotransmitters and mental disorders, while few studies collect and analyze the dispersed research results in systematic ways. We therefore gather the dispersed results that in the existing studies into a structured knowledge base for identifying and predicting the potential relationships between gut microbiota and mental disorders. In this study, we propose to construct a gut microbiota knowledge graph for mental disorder, which named as MiKG4MD. It is extendable by linking to future ontologies by just adding new relationships between existing information and new entities. This extendibility is emphasized for the integration with existing popular ontologies/terminologies, e.g. UMLS, MeSH, and KEGG. We demonstrate the performance of MiKG4MD with three SPARQL query test cases. Results show that the MiKG4MD knowledge graph is an effective method to predict the relationships between gut microbiota and mental disorders.
References54
Newest
#1Nasiru Ishola Aliyu (University of Ilorin)H-Index: 1
#2Abdulrahaman Musbau Dogo (University of Ilorin)H-Index: 1
Last. Tosho Abdurauf (Al-Hikmah University)H-Index: 1
view all 4 authors...
Cyberbullying is a type of cybercrime that involves the use of the internet and other information technology resources to deliberately insult, embarrass, harass, bully, and threaten people online. The ubiquity of internet connectivity has enabled an increase in the volume and pace of cyberbullying activities because the criminals no longer need to be physically present when committing the crime. This work aims to analyze and predict cyberbullying on Facebook using Naive Bayes algorithm. The scor...
Source
#1Siuly Siuly (VU: Victoria University, Australia)H-Index: 19
#2Xiangliang Zhang (KAUST: King Abdullah University of Science and Technology)H-Index: 34
Source
#1Ellionore Järbrink-Sehgal (BCM: Baylor College of Medicine)H-Index: 1
#2Anna Andreasson (KI: Karolinska Institutet)H-Index: 29
A growing body of evidence point toward the bidirectional gut microbiota–brain axis playing a role in mental health. Most of this research is conducted on animals why we in this review summarize and comment upon recent studies evaluating the gut microbiome in mental health in humans. Further support for the relevance of the bidirectional gut microbiota–brain communication in mood disorders has been presented, such as the effect of probiotics on brain connectivity and mental health outcomes and p...
Source
#1Ting LiuH-Index: 2
#2K. Anton FeenstraH-Index: 20
Last. Zhisheng HuangH-Index: 22
view all 4 authors...
Source
#1Gongjin Lan (VU: VU University Amsterdam)H-Index: 9
#2Jakub M. Tomczak (VU: VU University Amsterdam)H-Index: 20
Last. A. E. Eiben (VU: VU University Amsterdam)H-Index: 55
view all 4 authors...
Not all generate-and-test search algorithms are created equal. Bayesian Optimization (BO) invests a lot of computation time to generate the candidate solution that best balances the predicted value and the uncertainty given all previous data, taking increasingly more time as the number of evaluations performed grows. Evolutionary Algorithms (EA) on the other hand rely on search heuristics that typically do not depend on all previous data and can be done in constant time. Both the BO and EA commu...
#2Denis MareschalH-Index: 40
Last. Iroise DumontheilH-Index: 29
view all 3 authors...
Source
#1Aki Koivu (UTU: University of Turku)H-Index: 4
#2Mikko Sairanen (PerkinElmer)H-Index: 9
Modelling the risk of abnormal pregnancy-related outcomes such as stillbirth and preterm birth have been proposed in the past. Commonly they utilize maternal demographic and medical history information as predictors, and they are based on conventional statistical modelling techniques. In this study, we utilize state-of-the-art machine learning methods in the task of predicting early stillbirth, late stillbirth and preterm birth pregnancies. The aim of this experimentation is to discover novel ri...
Source
#1Xin Li (THU: Tsinghua University)H-Index: 130
#2Haoyang Liu (Beijing University of Posts and Telecommunications)H-Index: 1
Last. Chunxiao Xing (THU: Tsinghua University)H-Index: 16
view all 5 authors...
In this study, a medical knowledge graph is constructed from the electronic medical record text of knee osteoarthritis patients to support intelligent medical applications such as knowledge retrieval and decision support, and to promote the sharing of medical resources. After constructing the domain ontology of knee osteoarthritis and manually labeling, we trained a machine learning model to automatically perform entity recognition and entity relation extraction, and then used a graph database t...
Source
#1Thuan Pham (University of Southern Queensland)H-Index: 3
#2Xiaohui Tao (University of Southern Queensland)H-Index: 16
Last. Jianming Yong (University of Southern Queensland)H-Index: 18
view all 4 authors...
Applying Pearson correlation and semantic relations in building a heterogeneous information graph (HIG) to develop a classification model has achieved a notable performance in improving the accuracy of predicting the status of health risks. In this study, the approach that was used, integrated knowledge of the medical domain as well as taking advantage of applying Pearson correlation and semantic relations in building a classification model for diagnosis. The research mined knowledge which was e...
Source
#1Gongjin Lan (VU: VU University Amsterdam)H-Index: 9
#2Matteo De Carlo (VU: VU University Amsterdam)H-Index: 6
Last. A. E. Eiben (VU: VU University Amsterdam)H-Index: 55
view all 6 authors...
We generalize the well-studied problem of gait learning in modular robots in two dimensions. Firstly, we address locomotion in a given target direction that goes beyond learning a typical undirected gait. Secondly, rather than studying one fixed robot morphology we consider a test suite of different modular robots. This study is based on our interest in evolutionary robot systems where both morphologies and controllers evolve. In such a system, newborn robots have to learn to control their own b...
Cited By1
Newest
This website uses cookies.
We use cookies to improve your online experience. By continuing to use our website we assume you agree to the placement of these cookies.
To learn more, you can find in our Privacy Policy.