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                        "title": "BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models",
                        "abstract": "Background: Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification.Description: BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various formats. Reaction network diagrams generated from the models are also available in several formats. BioModels Database also provides features such as online simulation and the extraction of components from large scale models into smaller submodels. Finally, the system provides a range of web services that external software systems can use to access up-to-date data from the database.Conclusions: BioModels Database has become a recognised reference resource for systems biology. It is being used by the community in a variety of ways; for example, it is used to benchmark different simulation systems, and to study the clustering of models based upon their annotations. Model deposition to the database today is advised by several publishers of scientific journals. The models in BioModels Database are freely distributed and reusable; the underlying software infrastructure is also available from SourceForge https://sourceforge.net/projects/biomodels/ under the GNU General Public License. © 2010 Li et al; licensee BioMed Central Ltd.",
                        "date": "2010-06-29T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Li C."
                            },
                            {
                                "name": "Donizelli M."
                            },
                            {
                                "name": "Rodriguez N."
                            },
                            {
                                "name": "Dharuri H."
                            },
                            {
                                "name": "Endler L."
                            },
                            {
                                "name": "Chelliah V."
                            },
                            {
                                "name": "Li L."
                            },
                            {
                                "name": "He E."
                            },
                            {
                                "name": "Henry A."
                            },
                            {
                                "name": "Stefan M.I."
                            },
                            {
                                "name": "Snoep J.L."
                            },
                            {
                                "name": "Hucka M."
                            },
                            {
                                "name": "Le Novere N."
                            },
                            {
                                "name": "Laibe C."
                            }
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                        "journal": "BMC Systems Biology"
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                    "doi": "10.1093/nar/gkv1352",
                    "pmid": null,
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                    "metadata": {
                        "title": "The European Bioinformatics Institute in 2016: Data growth and integration",
                        "abstract": "© The Author(s) 2015.New technologies are revolutionising biological research and its applications by making it easier and cheaper to generate ever-greater volumes and types of data. In response, the services and infrastructure of the European Bioinformatics Institute (EMBL-EBI, www.ebi.ac.UK) are continually expanding: total disk capacity increases significantly every year to keep pace with demand (75 petabytes as of December 2015), and interoperability between resources remains a strategic priority. Since 2014 we have launched two new resources: the European Variation Archive for genetic variation data and EMPIAR for two-dimensional electron microscopy data, as well as a Resource Description Framework platform. We also launched the Embassy Cloud service, which allows users to run large analyses in a virtual environment next to EMBL-EBI's vast public data resources.",
                        "date": "2016-01-01T00:00:00Z",
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                        "authors": [
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                                "name": "Cook C.E."
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                            {
                                "name": "Bergman M.T."
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                            {
                                "name": "Finn R.D."
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                            {
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                            {
                                "name": "Apweiler R."
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                        "journal": "Nucleic Acids Research"
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                        "title": "DisGeNET: A discovery platform for the dynamical exploration of human diseases and their genes",
                        "abstract": "© The Author(s) 2015. Published by Oxford University Press.DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380 000 associations between >16 000 genes and 13 000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners.",
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                        "authors": [
                            {
                                "name": "Pinero J."
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                            {
                                "name": "Queralt-Rosinach N."
                            },
                            {
                                "name": "Bravo A."
                            },
                            {
                                "name": "Deu-Pons J."
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                            {
                                "name": "Bauer-Mehren A."
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                            {
                                "name": "Baron M."
                            },
                            {
                                "name": "Sanz F."
                            },
                            {
                                "name": "Furlong L.I."
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                        "title": "DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants",
                        "abstract": "© The Author(s) 2016.The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.",
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                        "authors": [
                            {
                                "name": "Pinero J."
                            },
                            {
                                "name": "Bravo A."
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                            {
                                "name": "Queralt-Rosinach N."
                            },
                            {
                                "name": "Gutierrez-Sacristan A."
                            },
                            {
                                "name": "Deu-Pons J."
                            },
                            {
                                "name": "Centeno E."
                            },
                            {
                                "name": "Garcia-Garcia J."
                            },
                            {
                                "name": "Sanz F."
                            },
                            {
                                "name": "Furlong L.I."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/bioinformatics/btq538",
                    "pmid": null,
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                    "type": [
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                    ],
                    "version": null,
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                    "metadata": {
                        "title": "DisGeNET: A Cytoscape plugin to visualize, integrate, search and analyze gene-disease networks",
                        "abstract": "Summary: DisGeNET is a plugin for Cytoscape to query and analyze human gene-disease networks. DisGeNET allows user-friendly access to a new gene-disease database that we have developed by integrating data from several public sources. DisGeNET permits queries restricted to (i) the original data source, (ii) the association type, (iii) the disease class or (iv) specific gene(s)/disease(s). It represents gene-disease associations in terms of bipartite graphs and provides gene centric and disease centric views of the data. It assists the user in the interpretation and exploration of the genetic basis of human diseases by a variety of built-in functions. Moreover, DisGeNET permits multicolouring of nodes (genes/diseases) according to standard disease classification for expedient visualization. © The Author 2010. Published by Oxford University Press. All rights reserved.",
                        "date": "2010-11-01T00:00:00Z",
                        "citationCount": 139,
                        "authors": [
                            {
                                "name": "Bauer-Mehren A."
                            },
                            {
                                "name": "Rautschka M."
                            },
                            {
                                "name": "Sanz F."
                            },
                            {
                                "name": "Furlong L.I."
                            }
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                        "journal": "Bioinformatics"
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                    "metadata": {
                        "title": "DisGeNET-RDF: Harnessing the innovative power of the Semantic Web to explore the genetic basis of diseases",
                        "abstract": "© 2016 The Author 2016. Published by Oxford University Press.Motivation: DisGeNET-RDF makes available knowledge on the genetic basis of human diseases in the Semantic Web. Gene-disease associations (GDAs) and their provenance metadata are published as human-readable and machine-processable web resources. The information on GDAs included in DisGeNET-RDF is interlinked to other biomedical databases to support the development of bioinformatics approaches for translational research through evidence-based exploitation of a rich and fully interconnected linked open data. Availability and implementation: http://rdf.disgenet.org/ Contact:",
                        "date": "2016-07-15T00:00:00Z",
                        "citationCount": 32,
                        "authors": [
                            {
                                "name": "Queralt-Rosinach N."
                            },
                            {
                                "name": "Pinero J."
                            },
                            {
                                "name": "Bravo A."
                            },
                            {
                                "name": "Sanz F."
                            },
                            {
                                "name": "Furlong L.I."
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                        "journal": "Bioinformatics"
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                    "metadata": {
                        "title": "Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases",
                        "abstract": "Background: Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. Principal Findings: We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. Conclusions: For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. Availability: The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download. © 2011 Bauer-Mehren et al.",
                        "date": "2011-06-20T00:00:00Z",
                        "citationCount": 112,
                        "authors": [
                            {
                                "name": "Bauer-Mehren A."
                            },
                            {
                                "name": "Bundschus M."
                            },
                            {
                                "name": "Rautschka M."
                            },
                            {
                                "name": "Mayer M.A."
                            },
                            {
                                "name": "Sanz F."
                            },
                            {
                                "name": "Furlong L.I."
                            }
                        ],
                        "journal": "PLoS ONE"
                    }
                },
                {
                    "doi": "10.1093/nar/gkw943",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
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                    "metadata": null
                },
                {
                    "doi": "10.1093/nar/gkz1021",
                    "pmid": null,
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                    "note": null,
                    "metadata": {
                        "title": "The DisGeNET knowledge platform for disease genomics: 2019 update",
                        "abstract": "© 2019 The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-Associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.",
                        "date": "2020-01-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Pinero J."
                            },
                            {
                                "name": "Ramirez-Anguita J.M."
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                            {
                                "name": "Sauch-Pitarch J."
                            },
                            {
                                "name": "Ronzano F."
                            },
                            {
                                "name": "Centeno E."
                            },
                            {
                                "name": "Sanz F."
                            },
                            {
                                "name": "Furlong L.I."
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                        ],
                        "journal": "Nucleic Acids Research"
                    }
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                    "doi": "10.1109/TCBB.2021.3083277",
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                        "title": "MultiTrans: an algorithm for path extraction through mixed integer linear programming for transcriptome assembly",
                        "abstract": "IEEERecent advances in RNA-seq technology have made identification of expressed genes affordable, and thus boosting repaid development of transcriptomic studies. Transcriptome assembly, reconstructing all expressed transcripts from RNA-seq reads, is an essential step to understand genes, proteins, and cell functions. Transcriptome assembly remains a challenging problem due to complications in splicing variants, expression levels, uneven coverage and sequencing errors. Here, we formulate the transcriptome assembly problem as path extraction on splicing graphs (or assembly graphs), and propose a novel algorithm MultiTrans for path extraction using mixed integer linear programming. MultiTrans is able to take into consideration coverage constraints on vertices and edges, the number of paths and the paired-end information simultaneously. We benchmarked MultiTrans against two state-of-the-art transcriptome assemblers, TransLiG and rnaSPAdes. Experimental results show that MultiTrans generates more accurate transcripts compared to TransLiG (using the same splicing graphs) and rnaSPAdes (using the same assembly graphs). MultiTrans is freely available at https://github.com/jzbio/MultiTrans.",
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                                "name": "Zhao J."
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                            {
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                            "uri": "http://edamontology.org/operation_3899",
                            "term": "Protein-protein docking"
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                            "uri": "http://edamontology.org/operation_0244",
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                    "term": "Molecular modelling"
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                    "term": "Drug discovery"
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                {
                    "doi": "10.1021/ACS.JCIM.0C01358",
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                    "metadata": {
                        "title": "MSLDOCK: Multi-Swarm Optimization for Flexible Ligand Docking and Virtual Screening",
                        "abstract": "© Autodock and its various variants are widely utilized docking approaches, which adopt optimization methods as search algorithms for flexible ligand docking and virtual screening. However, many of them have their limitations, such as poor accuracy for dockings with highly flexible ligands and low docking efficiency. In this paper, a multi-swarm optimization algorithm integrated with Autodock environment is proposed to design a high-performance and high-efficiency docking program, namely, MSLDOCK. The search algorithm is a combination of the random drift particle swarm optimization with a novel multi-swarm strategy and the Solis and Wets local search method with a modified implementation. Due to the algorithm's structure, MSLDOCK also has a multithread mode. The experimental results reveal that MSLDOCK outperforms other two Autodock-based approaches in many aspects, such as self-docking, cross-docking, and virtual screening accuracies as well as docking efficiency. Moreover, compared with three non-Autodock-based docking programs, MSLDOCK can be a reliable choice for self-docking and virtual screening, especially for dealing with highly flexible ligand docking problems. The source code of MSLDOCK can be downloaded for free from https://github.com/lcmeteor/MSLDOCK.",
                        "date": "2021-03-22T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Li C."
                            },
                            {
                                "name": "Sun J."
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                            {
                                "name": "Palade V."
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                        ],
                        "journal": "Journal of Chemical Information and Modeling"
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                    "name": "Jun Sun",
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                            "term": "Phasing"
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                    "term": "Machine learning"
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                    "term": "Infectious disease"
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                    "uri": "http://edamontology.org/topic_3422",
                    "term": "Urology and nephrology"
                },
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                    "uri": "http://edamontology.org/topic_3334",
                    "term": "Neurology"
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                    "doi": "10.1038/S41598-021-84781-X",
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                    "metadata": {
                        "title": "Individualized prediction of COVID-19 adverse outcomes with MLHO",
                        "abstract": "© 2021, The Author(s).The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients’ past medical records (before their COVID-19 infection). MLHO’s architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients’ pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge.",
                        "date": "2021-12-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Estiri H."
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                            {
                                "name": "Strasser Z.H."
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                            {
                                "name": "Murphy S.N."
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                        ],
                        "journal": "Scientific Reports"
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                    "metadata": {
                        "title": "miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data",
                        "abstract": "© 2021 Informa UK Limited, trading as Taylor & Francis Group.In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, miRSM, for facilitating the procedure of inferring and analysing miRNA sponge modules. miRSM provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. miRSM will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.",
                        "date": "2021-01-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Zhang J."
                            },
                            {
                                "name": "Liu L."
                            },
                            {
                                "name": "Xu T."
                            },
                            {
                                "name": "Zhang W."
                            },
                            {
                                "name": "Zhao C."
                            },
                            {
                                "name": "Li S."
                            },
                            {
                                "name": "Li J."
                            },
                            {
                                "name": "Rao N."
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                            {
                                "name": "Le T.D."
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                        "journal": "RNA Biology"
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