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            "name": "Virtual Fly Brain",
            "description": "Interactive atlas of the Drosophila nervous system.",
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                            "uri": "http://edamontology.org/operation_2421",
                            "term": "Database search"
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                        {
                            "uri": "http://edamontology.org/operation_3450",
                            "term": "Neurite measurement"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3553",
                            "term": "Image annotation"
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                    "note": null,
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                "Web application"
            ],
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                {
                    "uri": "http://edamontology.org/topic_3304",
                    "term": "Neurobiology"
                },
                {
                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
                },
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                    "uri": "http://edamontology.org/topic_0089",
                    "term": "Ontology and terminology"
                },
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                    "uri": "http://edamontology.org/topic_3382",
                    "term": "Imaging"
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                {
                    "uri": "http://edamontology.org/topic_3068",
                    "term": "Literature and language"
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                {
                    "doi": "10.3389/FPHYS.2023.1076533",
                    "pmid": "36776967",
                    "pmcid": "PMC9908962",
                    "type": [],
                    "version": null,
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                    "metadata": {
                        "title": "Virtual Fly Brain—An interactive atlas of the Drosophila nervous system",
                        "abstract": "As a model organism, Drosophila is uniquely placed to contribute to our understanding of how brains control complex behavior. Not only does it have complex adaptive behaviors, but also a uniquely powerful genetic toolkit, increasingly complete dense connectomic maps of the central nervous system and a rapidly growing set of transcriptomic profiles of cell types. But this also poses a challenge: Given the massive amounts of available data, how are researchers to Find, Access, Integrate and Reuse (FAIR) relevant data in order to develop an integrated anatomical and molecular picture of circuits, inform hypothesis generation, and find reagents for experiments to test these hypotheses? The Virtual Fly Brain (virtualflybrain.org) web application & API provide a solution to this problem, using FAIR principles to integrate 3D images of neurons and brain regions, connectomics, transcriptomics and reagent expression data covering the whole CNS in both larva and adult. Users can search for neurons, neuroanatomy and reagents by name, location, or connectivity, via text search, clicking on 3D images, search-by-image, and queries by type (e.g., dopaminergic neuron) or properties (e.g., synaptic input in the antennal lobe). Returned results include cross-registered 3D images that can be explored in linked 2D and 3D browsers or downloaded under open licenses, and extensive descriptions of cell types and regions curated from the literature. These solutions are potentially extensible to cover similar atlasing and data integration challenges in vertebrates.",
                        "date": "2023-01-26T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Court R."
                            },
                            {
                                "name": "Costa M."
                            },
                            {
                                "name": "Pilgrim C."
                            },
                            {
                                "name": "Millburn G."
                            },
                            {
                                "name": "Holmes A."
                            },
                            {
                                "name": "McLachlan A."
                            },
                            {
                                "name": "Larkin A."
                            },
                            {
                                "name": "Matentzoglu N."
                            },
                            {
                                "name": "Kir H."
                            },
                            {
                                "name": "Parkinson H."
                            },
                            {
                                "name": "Brown N.H."
                            },
                            {
                                "name": "O'Kane C.J."
                            },
                            {
                                "name": "Armstrong J.D."
                            },
                            {
                                "name": "Jefferis G.S.X.E."
                            },
                            {
                                "name": "Osumi-Sutherland D."
                            }
                        ],
                        "journal": "Frontiers in Physiology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "David Osumi-Sutherland",
                    "email": "davidos@ebi.ac.uk",
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                {
                    "name": "Robert Court",
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                    "name": "Marta Costa",
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                    "name": "Gregory S X E Jefferis",
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            "name": "WMDS.net",
            "description": "A network control framework for identifying key players in transcriptome programs.",
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                            "term": "Network analysis"
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                            "term": "Validation"
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                        {
                            "uri": "http://edamontology.org/operation_3463",
                            "term": "Expression correlation analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0314",
                            "term": "Gene expression profiling"
                        }
                    ],
                    "input": [],
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                "Library"
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                {
                    "uri": "http://edamontology.org/topic_2640",
                    "term": "Oncology"
                },
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                },
                {
                    "uri": "http://edamontology.org/topic_0203",
                    "term": "Gene expression"
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                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
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                    "uri": "http://edamontology.org/topic_3300",
                    "term": "Physiology"
                }
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                "MATLAB"
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                {
                    "doi": "10.1093/BIOINFORMATICS/BTAD071",
                    "pmid": "36727489",
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                    "type": [],
                    "version": null,
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                    "metadata": {
                        "title": "WMDS.net: a network control framework for identifying key players in transcriptome programs",
                        "abstract": "MOTIVATION: Mammalian cells can be transcriptionally reprogramed to other cellular phenotypes. Controllability of such complex transitions in transcriptional networks underlying cellular phenotypes is an inherent biological characteristic. This network controllability can be interpreted by operating a few key regulators to guide the transcriptional program from one state to another. Finding the key regulators in the transcriptional program can provide key insights into the network state transition underlying cellular phenotypes. RESULTS: To address this challenge, here, we proposed to identify the key regulators in the transcriptional co-expression network as a minimum dominating set (MDS) of driver nodes that can fully control the network state transition. Based on the theory of structural controllability, we developed a weighted MDS network model (WMDS.net) to find the driver nodes of differential gene co-expression networks. The weight of WMDS.net integrates the degree of nodes in the network and the significance of gene co-expression difference between two physiological states into the measurement of node controllability of the transcriptional network. To confirm its validity, we applied WMDS.net to the discovery of cancer driver genes in RNA-seq datasets from The Cancer Genome Atlas. WMDS.net is powerful among various cancer datasets and outperformed the other top-tier tools with a better balance between precision and recall. AVAILABILITY AND IMPLEMENTATION: https://github.com/chaofen123/WMDS.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
                        "date": "2023-02-14T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Cheng X."
                            },
                            {
                                "name": "Amanullah M."
                            },
                            {
                                "name": "Liu W."
                            },
                            {
                                "name": "Liu Y."
                            },
                            {
                                "name": "Pan X."
                            },
                            {
                                "name": "Zhang H."
                            },
                            {
                                "name": "Xu H."
                            },
                            {
                                "name": "Liu P."
                            },
                            {
                                "name": "Lu Y."
                            }
                        ],
                        "journal": "Bioinformatics (Oxford, England)"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Pengyuan Liu",
                    "email": "pyliu@zju.edu.cn",
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                {
                    "name": "Yan Lu",
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                {
                    "name": "Xiang Cheng",
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                {
                    "name": "Md Amanullah",
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            "name": "Wordom",
            "description": "A user-friendly program for the analysis of molecular structures and conformational ensembles.",
            "homepage": "http://wordom.sf.net",
            "biotoolsID": "pub2tools2021__wordom",
            "biotoolsCURIE": "biotools:pub2tools2021__wordom",
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                {
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                        {
                            "uri": "http://edamontology.org/operation_3891",
                            "term": "Essential dynamics"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2476",
                            "term": "Molecular dynamics"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2939",
                            "term": "Principal component visualisation"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
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                {
                    "uri": "http://edamontology.org/topic_0176",
                    "term": "Molecular dynamics"
                },
                {
                    "uri": "http://edamontology.org/topic_3175",
                    "term": "Structural variation"
                },
                {
                    "uri": "http://edamontology.org/topic_0593",
                    "term": "NMR"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
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                "Python"
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            "link": [
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                    "url": "https://sourceforge.net/projects/wordom/",
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            "publication": [
                {
                    "doi": "10.1016/J.CSBJ.2023.01.026",
                    "pmid": "36817953",
                    "pmcid": "PMC9929209",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Wordom update 2: A user-friendly program for the analysis of molecular structures and conformational ensembles",
                        "abstract": "We present the second update of Wordom, a user-friendly and efficient program for manipulation and analysis of conformational ensembles from molecular simulations. The actual update expands some of the existing modules and adds 21 new modules to the update 1 published in 2011. The new adds can be divided into three sets that: 1) analyze atomic fluctuations and structural communication; 2) explore ion-channel conformational dynamics and ionic translocation; and 3) compute geometrical indices of structural deformation. Set 1 serves to compute correlations of motions, find geometrically stable domains, identify a dynamically invariant core, find changes in domain-domain separation and mutual orientation, perform wavelet analysis of large-scale simulations, process the output of principal component analysis of atomic fluctuations, perform functional mode analysis, infer regions of mechanical rigidity, analyze overall fluctuations, and perform the perturbation response scanning. Set 2 includes modules specific for ion channels, which serve to monitor the pore radius as well as water or ion fluxes, and measure functional collective motions like receptor twisting or tilting angles. Finally, set 3 includes tools to monitor structural deformations by computing angles, perimeter, area, volume, β-sheet curvature, radial distribution function, and center of mass. The ring perception module is also included, helpful to monitor supramolecular self-assemblies. This update places Wordom among the most suitable, complete, user-friendly, and efficient software for the analysis of biomolecular simulations. The source code of Wordom and the relative documentation are available under the GNU general public license at http://wordom.sf.net.",
                        "date": "2023-01-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Felline A."
                            },
                            {
                                "name": "Conti S."
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                            {
                                "name": "Seeber M."
                            },
                            {
                                "name": "Cecchini M."
                            },
                            {
                                "name": "Fanelli F."
                            }
                        ],
                        "journal": "Computational and Structural Biotechnology Journal"
                    }
                }
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                    "name": "Angelo Felline",
                    "email": "angelo.felline@unimore.it",
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                {
                    "name": "Francesca Fanelli",
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            "name": "Yersiniomics",
            "description": "Multi-Omics Interactive Database for Yersinia Species.",
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                            "term": "Data retrieval"
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                    "uri": "http://edamontology.org/topic_0102",
                    "term": "Mapping"
                },
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
                },
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                    "uri": "http://edamontology.org/topic_3520",
                    "term": "Proteomics experiment"
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                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                }
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                "Windows"
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                    "name": "Pierre Lê-Bury",
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                    "name": "Javier Pizarro-Cerdá",
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                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-9924-0837",
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            "name": "M2REMAP",
            "description": "Multimodal representation learning for predicting molecule-disease relations.\n\nThis repository contains the code and data for \"Multimodal Representation Learning for Predicting Molecule-Disease Relations\".\n\nWe introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular- disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients.\n\nPredicting molecule-disease indications and side effects is important for drug development and pharmacovigilance.",
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                            "uri": "http://edamontology.org/operation_3625",
                            "term": "Relation extraction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3891",
                            "term": "Essential dynamics"
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                            "uri": "http://edamontology.org/operation_3938",
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                    "term": "Pharmacovigilance"
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                    "term": "Machine learning"
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                    "term": "Drug development"
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                        "title": "Multimodal representation learning for predicting molecule-disease relations",
                        "abstract": "© The Author(s) 2023. Published by Oxford University Press.MOTIVATION: Predicting molecule-disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule-molecule, molecule-disease and disease-disease semantic dependencies can potentially improve prediction performance. METHODS: We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule-disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. RESULTS: We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
                        "date": "2023-02-03T00:00:00Z",
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                            {
                                "name": "Wen J."
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                            {
                                "name": "Zhang X."
                            },
                            {
                                "name": "Rush E."
                            },
                            {
                                "name": "Panickan V.A."
                            },
                            {
                                "name": "Li X."
                            },
                            {
                                "name": "Cai T."
                            },
                            {
                                "name": "Zhou D."
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                            {
                                "name": "Ho Y.-L."
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                            {
                                "name": "Costa L."
                            },
                            {
                                "name": "Begoli E."
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                            {
                                "name": "Hong C."
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                            {
                                "name": "Gaziano J.M."
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                            {
                                "name": "Lu J."
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                            {
                                "name": "Liao K.P."
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                            {
                                "name": "Zitnik M."
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                            {
                                "name": "Cai T."
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                        "journal": "Bioinformatics (Oxford, England)"
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