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Wang H, Zheng H, Chen DZ. TANGO: A GO-Term Embedding Based Method for Protein Semantic Similarity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:694-706. [PMID: 35030084 DOI: 10.1109/tcbb.2022.3143480] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We aim to quantitatively predict protein semantic similarities (PSS), which is vital to making biological discoveries. Previously, researchers commonly exploited Gene Ontology (GO) graphs (containing standardized hierarchically-organized GO terms for annotating distinct protein attributes) to learn GO term embeddings (vector representations) for quantifying protein attribute similarities and aggregate these embeddings to form protein embeddings for similarity measurement. However, two key properties of GO terms and annotated proteins are not yet well-explored by these learning-based methods: (1) taxonomy relations between GO terms; (2) GO terms' different contributions in describing protein semantics. In this paper, we propose TANGO, a new framework composed of a TAxoNomy-aware embedding module and an aggreGatiOn module. Our Embedding Module encodes taxonomic information into GO term embeddings by incorporating GO term topological distances in the GO graph hierarchy. Hence, distances between GO term embeddings can be used to more accurately measure shared meanings between correlated protein attributes. Our Aggregation Module automatically determines the contributions of GO terms when merging into the target protein embeddings, by mining GO term concept dependency relations in the GO graph and correlations in protein annotations. We conduct extensive experiments on several public datasets. On two PSS metrics, our new method significantly outperforms known methods by a large margin.
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2
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Eremeev AP, Muntyan ER. Developing an Ontology on the Basis of Graphs with Multiple and Heterotypic Connections. SCIENTIFIC AND TECHNICAL INFORMATION PROCESSING 2022. [DOI: 10.3103/s0147688222060041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
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3
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A Contemporary Review on Utilizing Semantic Web Technologies in Healthcare, Virtual Communities, and Ontology-Based Information Processing Systems. ELECTRONICS 2022. [DOI: 10.3390/electronics11030453] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The semantic web is an emerging technology that helps to connect different users to create their content and also facilitates the way of representing information in a manner that can be made understandable for computers. As the world is heading towards the fourth industrial revolution, the implicit utilization of artificial-intelligence-enabled semantic web technologies paves the way for many real-time application developments. The fundamental building blocks for the overwhelming utilization of semantic web technologies are ontologies, and it allows sharing as well as reusing the concepts in a standardized way so that the data gathered from heterogeneous sources receive a common nomenclature, and it paves the way for disambiguating the duplicates very easily. In this context, the right utilization of ontology capabilities would further strengthen its presence in many web-based applications such as e-learning, virtual communities, social media sites, healthcare, agriculture, etc. In this paper, we have given the comprehensive review of using the semantic web in the domain of healthcare, some virtual communities, and other information retrieval projects. As the role of semantic web is becoming pervasive in many domains, the demand for the semantic web in healthcare, virtual communities, and information retrieval has been gaining huge momentum in recent years. To obtain the correct sense of the meaning of the words or terms given in the textual content, it is deemed necessary to apply the right ontology to fix the ambiguity and shun any deviations that persist on the concepts. In this review paper, we have highlighted all the necessary information for a good understanding of the semantic web and its ontological frameworks.
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Kulmanov M, Smaili FZ, Gao X, Hoehndorf R. Semantic similarity and machine learning with ontologies. Brief Bioinform 2021; 22:bbaa199. [PMID: 33049044 PMCID: PMC8293838 DOI: 10.1093/bib/bbaa199] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 12/13/2022] Open
Abstract
Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in ontologies and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies.
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Affiliation(s)
| | | | - Xin Gao
- Computational Bioscience Research Center and lead of the Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology
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5
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Lou P, Dong Y, Jimeno Yepes A, Li C. A representation model for biological entities by fusing structured axioms with unstructured texts. Bioinformatics 2021; 37:1156-1163. [PMID: 33107905 DOI: 10.1093/bioinformatics/btaa913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 09/04/2020] [Accepted: 10/13/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Structured semantic resources, for example, biological knowledge bases and ontologies, formally define biological concepts, entities and their semantic relationships, manifested as structured axioms and unstructured texts (e.g. textual definitions). The resources contain accurate expressions of biological reality and have been used by machine-learning models to assist intelligent applications like knowledge discovery. The current methods use both the axioms and definitions as plain texts in representation learning (RL). However, since the axioms are machine-readable while the natural language is human-understandable, difference in meaning of token and structure impedes the representations to encode desirable biological knowledge. RESULTS We propose ERBK, a RL model of bio-entities. Instead of using the axioms and definitions as a textual corpus, our method uses knowledge graph embedding method and deep convolutional neural models to encode the axioms and definitions respectively. The representations could not only encode more underlying biological knowledge but also be further applied to zero-shot circumstance where existing approaches fall short. Experimental evaluations show that ERBK outperforms the existing methods for predicting protein-protein interactions and gene-disease associations. Moreover, it shows that ERBK still maintains promising performance under the zero-shot circumstance. We believe the representations and the method have certain generality and could extend to other types of bio-relation. AVAILABILITY AND IMPLEMENTATION The source code is available at the gitlab repository https://gitlab.com/BioAI/erbk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Peiliang Lou
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.,Key Laboratory of Intelligent Networks and Network Security (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi 710049, China
| | - YuXin Dong
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | | | - Chen Li
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.,National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
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6
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Chen J, Althagafi A, Hoehndorf R. Predicting candidate genes from phenotypes, functions and anatomical site of expression. Bioinformatics 2021; 37:853-860. [PMID: 33051643 PMCID: PMC8248315 DOI: 10.1093/bioinformatics/btaa879] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/26/2020] [Accepted: 09/28/2020] [Indexed: 12/30/2022] Open
Abstract
Motivation Over the past years, many computational methods have been developed to
incorporate information about phenotypes for disease–gene
prioritization task. These methods generally compute the similarity between
a patient’s phenotypes and a database of gene-phenotype to find the
most phenotypically similar match. The main limitation in these methods is
their reliance on knowledge about phenotypes associated with particular
genes, which is not complete in humans as well as in many model organisms,
such as the mouse and fish. Information about functions of gene products and
anatomical site of gene expression is available for more genes and can also
be related to phenotypes through ontologies and machine-learning models. Results We developed a novel graph-based machine-learning method for biomedical
ontologies, which is able to exploit axioms in ontologies and other
graph-structured data. Using our machine-learning method, we embed genes
based on their associated phenotypes, functions of the gene products and
anatomical location of gene expression. We then develop a machine-learning
model to predict gene–disease associations based on the associations
between genes and multiple biomedical ontologies, and this model
significantly improves over state-of-the-art methods. Furthermore, we extend
phenotype-based gene prioritization methods significantly to all genes,
which are associated with phenotypes, functions or site of expression. Availability and implementation Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec. Supplementary information Supplementary data
are available at Bioinformatics online.
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Affiliation(s)
- Jun Chen
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Azza Althagafi
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia.,Computer Science Department, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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Timón-Reina S, Rincón M, Martínez-Tomás R. An overview of graph databases and their applications in the biomedical domain. Database (Oxford) 2021; 2021:baab026. [PMID: 34003247 PMCID: PMC8130509 DOI: 10.1093/database/baab026] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/24/2021] [Accepted: 04/30/2021] [Indexed: 01/18/2023]
Abstract
Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.
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Affiliation(s)
- Santiago Timón-Reina
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
| | - Mariano Rincón
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
| | - Rafael Martínez-Tomás
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
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Alshahrani M, Hoehndorf R. Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes. Bioinformatics 2019; 34:i901-i907. [PMID: 30423077 PMCID: PMC6129260 DOI: 10.1093/bioinformatics/bty559] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Motivation In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease’s (or patient’s) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. Results We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprised of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network. Availability and implementation https://github.com/bio-ontology-research-group/SmuDGE
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Affiliation(s)
- Mona Alshahrani
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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Smaili FZ, Gao X, Hoehndorf R. Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations. Bioinformatics 2019; 34:i52-i60. [PMID: 29949999 PMCID: PMC6022543 DOI: 10.1093/bioinformatics/bty259] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Motivation Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications. Results We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the gene ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes. First, we demonstrate that Onto2Vec-generated feature vectors can significantly improve prediction of protein-protein interactions in human and yeast. We then illustrate how Onto2Vec representations provide the means for constructing data-driven, trainable semantic similarity measures that can be used to identify particular relations between proteins. Finally, we use an unsupervised clustering approach to identify protein families based on their Enzyme Commission numbers. Our results demonstrate that Onto2Vec can generate high quality feature vectors from biological entities and ontologies. Onto2Vec has the potential to significantly outperform the state-of-the-art in several predictive applications in which ontologies are involved. Availability and implementation https://github.com/bio-ontology-research-group/onto2vec. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fatima Zohra Smaili
- Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Robert Hoehndorf
- Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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10
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Essack M, Salhi A, Stanimirovic J, Tifratene F, Bin Raies A, Hungler A, Uludag M, Van Neste C, Trpkovic A, Bajic VP, Bajic VB, Isenovic ER. Literature-Based Enrichment Insights into Redox Control of Vascular Biology. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2019; 2019:1769437. [PMID: 31223421 PMCID: PMC6542245 DOI: 10.1155/2019/1769437] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 04/11/2019] [Accepted: 05/02/2019] [Indexed: 02/07/2023]
Abstract
In cellular physiology and signaling, reactive oxygen species (ROS) play one of the most critical roles. ROS overproduction leads to cellular oxidative stress. This may lead to an irrecoverable imbalance of redox (oxidation-reduction reaction) function that deregulates redox homeostasis, which itself could lead to several diseases including neurodegenerative disease, cardiovascular disease, and cancers. In this study, we focus on the redox effects related to vascular systems in mammals. To support research in this domain, we developed an online knowledge base, DES-RedoxVasc, which enables exploration of information contained in the biomedical scientific literature. The DES-RedoxVasc system analyzed 233399 documents consisting of PubMed abstracts and PubMed Central full-text articles related to different aspects of redox biology in vascular systems. It allows researchers to explore enriched concepts from 28 curated thematic dictionaries, as well as literature-derived potential associations of pairs of such enriched concepts, where associations themselves are statistically enriched. For example, the system allows exploration of associations of pathways, diseases, mutations, genes/proteins, miRNAs, long ncRNAs, toxins, drugs, biological processes, molecular functions, etc. that allow for insights about different aspects of redox effects and control of processes related to the vascular system. Moreover, we deliver case studies about some existing or possibly novel knowledge regarding redox of vascular biology demonstrating the usefulness of DES-RedoxVasc. DES-RedoxVasc is the first compiled knowledge base using text mining for the exploration of this topic.
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Affiliation(s)
- Magbubah Essack
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Adil Salhi
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Julijana Stanimirovic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
| | - Faroug Tifratene
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Arwa Bin Raies
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Arnaud Hungler
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Mahmut Uludag
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Christophe Van Neste
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Andreja Trpkovic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
| | - Vladan P. Bajic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
| | - Vladimir B. Bajic
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Thuwal, Saudi Arabia
| | - Esma R. Isenovic
- Vinca Institute, University of Belgrade, Laboratory for Molecular Endocrinology and Radiobiology, Belgrade, Serbia
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11
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Juckett DA, Kasten EP, Davis FN, Gostine M. Concept detection using text exemplars aligned with a specialized ontology. DATA KNOWL ENG 2019. [DOI: 10.1016/j.datak.2018.11.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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12
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Smaili FZ, Gao X, Hoehndorf R. OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction. Bioinformatics 2018; 35:2133-2140. [DOI: 10.1093/bioinformatics/bty933] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 11/02/2018] [Accepted: 11/07/2018] [Indexed: 12/11/2022] Open
Affiliation(s)
- Fatima Zohra Smaili
- Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Xin Gao
- Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Robert Hoehndorf
- Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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13
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Röttjers L, Faust K. From hairballs to hypotheses-biological insights from microbial networks. FEMS Microbiol Rev 2018; 42:761-780. [PMID: 30085090 PMCID: PMC6199531 DOI: 10.1093/femsre/fuy030] [Citation(s) in RCA: 262] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 07/24/2018] [Indexed: 12/19/2022] Open
Abstract
Microbial networks are an increasingly popular tool to investigate microbial community structure, as they integrate multiple types of information and may represent systems-level behaviour. Interpreting these networks is not straightforward, and the biological implications of network properties are unclear. Analysis of microbial networks allows researchers to predict hub species and species interactions. Additionally, such analyses can help identify alternative community states and niches. Here, we review factors that can result in spurious predictions and address emergent properties that may be meaningful in the context of the microbiome. We also give an overview of studies that analyse microbial networks to identify new hypotheses. Moreover, we show in a simulation how network properties are affected by tool choice and environmental factors. For example, hub species are not consistent across tools, and environmental heterogeneity induces modularity. We highlight the need for robust microbial network inference and suggest strategies to infer networks more reliably.
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Affiliation(s)
- Lisa Röttjers
- KU Leuven, Department of Microbiology and Immunology, Rega Institute, Laboratory of Molecular Bacteriology, Leuven, Belgium
| | - Karoline Faust
- KU Leuven, Department of Microbiology and Immunology, Rega Institute, Laboratory of Molecular Bacteriology, Leuven, Belgium
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