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            "name": "CiLiQuant",
            "description": "This tool separates junction reads based on their linear or circular origin. Only non-ambiguous junction reads are used to compare the relative linear and circular transcript abundance.\nTogether with the circ fraction (circ/(circ+lin)), a 95% confidence interval is provided.",
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                            "uri": "http://edamontology.org/operation_2441",
                            "term": "RNA structure prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2995",
                            "term": "Sequence classification"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3112",
                                "term": "Gene expression matrix"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3003",
                                    "term": "BED"
                                }
                            ]
                        },
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                            "data": {
                                "uri": "http://edamontology.org/data_3210",
                                "term": "Genome index"
                            },
                            "format": [
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                                    "uri": "http://edamontology.org/format_3003",
                                    "term": "BED"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0659",
                    "term": "Functional, regulatory and non-coding RNA"
                },
                {
                    "uri": "http://edamontology.org/topic_3320",
                    "term": "RNA splicing"
                }
            ],
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                "Linux",
                "Windows"
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                    "url": "https://github.com/almorlio/CiLiQuant",
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                    "name": "Annelien Morlion",
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            "name": "FlowSOM",
            "description": "Visualizing millions of cells.",
            "homepage": "https://github.com/SofieVG/FlowSOM",
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                            "uri": "http://edamontology.org/operation_0337",
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                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2600",
                                "term": "Pathway or network"
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                    "term": "Cell biology"
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                    "url": "https://github.com/SofieVG/FlowSOM",
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                    "doi": "10.1002/cyto.a.22625",
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                    "metadata": {
                        "title": "FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data",
                        "abstract": "© 2015 International Society for Advancement of Cytometry.The number of markers measured in both flow and mass cytometry keeps increasing steadily. Although this provides a wealth of information, it becomes infeasible to analyze these datasets manually. When using 2D scatter plots, the number of possible plots increases exponentially with the number of markers and therefore, relevant information that is present in the data might be missed. In this article, we introduce a new visualization technique, called FlowSOM, which analyzes Flow or mass cytometry data using a Self-Organizing Map. Using a two-level clustering and star charts, our algorithm helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. R code is available at https://github.com/SofieVG/FlowSOM and will be made available at Bioconductor.",
                        "date": "2015-01-01T00:00:00Z",
                        "citationCount": 374,
                        "authors": [
                            {
                                "name": "Van Gassen S."
                            },
                            {
                                "name": "Callebaut B."
                            },
                            {
                                "name": "Van Helden M.J."
                            },
                            {
                                "name": "Lambrecht B.N."
                            },
                            {
                                "name": "Demeester P."
                            },
                            {
                                "name": "Dhaene T."
                            },
                            {
                                "name": "Saeys Y."
                            }
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                        "journal": "Cytometry Part A"
                    }
                }
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                    "name": "ugent.be",
                    "email": null,
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                    "name": null,
                    "email": "Yvan.Saeys@ugent.be",
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        },
        {
            "name": "FastQC",
            "description": "This tool aims to provide a QC report which can spot problems or biases which originate either in the sequencer or in the starting library material. It can be run in one of two modes. It can either run as a stand alone interactive application for the immediate analysis of small numbers of FastQ files, or it can be run in a non-interactive mode where it would be suitable for integrating into a larger analysis pipeline for the systematic processing of large numbers of files.",
            "homepage": "http://www.bioinformatics.babraham.ac.uk/projects/fastqc/",
            "biotoolsID": "fastqc",
            "biotoolsCURIE": "biotools:fastqc",
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0236",
                            "term": "Sequence composition calculation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3218",
                            "term": "Sequencing quality control"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2238",
                            "term": "Statistical calculation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0848",
                                "term": "Raw sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2182",
                                    "term": "FASTQ-like format (text)"
                                },
                                {
                                    "uri": "http://edamontology.org/format_2573",
                                    "term": "SAM"
                                },
                                {
                                    "uri": "http://edamontology.org/format_1930",
                                    "term": "FASTQ"
                                },
                                {
                                    "uri": "http://edamontology.org/format_2572",
                                    "term": "BAM"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2955",
                                "term": "Sequence report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                }
                            ]
                        }
                    ],
                    "note": "The analysis in FastQC is performed by a series of analysis modules : (1) Basic statistics (2) Per base sequence quality (3) Per base sequence quality scores (4) Per base sequence content (5) Per base GC content (6) Per base N content (7) Sequence lenght distribituion (8) Duplicate sequences (9) Overrepresented sequences (10) Adaptater content (11) Kmer content (12) Per tile sequence quality",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Desktop application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3168",
                    "term": "Sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_3572",
                    "term": "Data quality management"
                },
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                }
            ],
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                "Windows"
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                "Java"
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                    "url": "https://galaxy.pasteur.fr/tool_runner?tool_id=fastqc",
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        {
            "name": "MobiDB",
            "description": "Database of protein disorder and mobility annotations.",
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                            "uri": "http://edamontology.org/operation_0224",
                            "term": "Query and retrieval"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2406",
                            "term": "Protein structure analysis"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3021",
                                "term": "UniProt accession"
                            },
                            "format": []
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2007",
                                "term": "UniProt keyword"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1964",
                                    "term": "plain text format (unformatted)"
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                            ]
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                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1255",
                                "term": "Sequence features"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3464",
                                    "term": "JSON"
                                },
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                }
                            ]
                        }
                    ],
                    "note": "Intrinsic disorder annotations of proteins RESTful access: use the following url:\nhttps://mobidb.org/api/download?free_text=p53",
                    "cmd": null
                }
            ],
            "toolType": [
                "Web API",
                "Web application",
                "Database portal"
            ],
            "topic": [
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                    "uri": "http://edamontology.org/topic_2814",
                    "term": "Protein structure analysis"
                },
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                    "term": "Protein disordered structure"
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            "documentation": [
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                    "url": "https://mobidb.org/help/apidoc",
                    "type": [
                        "API documentation"
                    ],
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                {
                    "url": "https://mobidb.org/about/mobidb",
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            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gku982",
                    "pmid": "25361972",
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                    "type": [
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                    ],
                    "version": "2",
                    "note": null,
                    "metadata": {
                        "title": "MobiDB 2.0: An improved database of intrinsically disordered and mobile proteins",
                        "abstract": "© The Author(s) 2014.MobiDB (http://mobidb.bio.unipd.it/) is a database of intrinsically disordered and mobile proteins. Intrinsically disordered regions are key for the function of numerous proteins. Here we provide a new version of MobiDB, a centralized source aimed at providing the most complete picture on different flavors of disorder in protein structures covering all UniProt sequences (currently over 80 million). The database features three levels of annotation: manually curated, indirect and predicted. Manually curated data is extracted from the DisProt database. Indirect data is inferred from PDB structures that are considered an indication of intrinsic disorder. The 10 predictors currently included (three ESpritz flavors, two IUPred flavors, two DisEMBL flavors, GlobPlot, VSL2b and JRONN) enable MobiDB to provide disorder annotations for every protein in absence of more reliable data. The new version also features a consensus annotation and classification for long disordered regions. In order to complement the disorder annotations, MobiDB features additional annotations from external sources. Annotations from the UniProt database include post-translational modifications and linear motifs. Pfam annotations are displayed in graphical form and are link-enabled, allowing the user to visit the corresponding Pfam page for further information. Experimental protein-protein interactions from STRING are also classified for disorder content.",
                        "date": "2015-01-28T00:00:00Z",
                        "citationCount": 139,
                        "authors": [
                            {
                                "name": "Potenza E."
                            },
                            {
                                "name": "Di Domenico T."
                            },
                            {
                                "name": "Walsh I."
                            },
                            {
                                "name": "Tosatto S.C.E."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/bioinformatics/bts327",
                    "pmid": "22661649",
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": "1",
                    "note": null,
                    "metadata": {
                        "title": "MobiDB: A comprehensive database of intrinsic protein disorder annotations",
                        "abstract": "Motivation: Disordered protein regions are key to the function of numerous processes within an organism and to the determination of a protein's biological role. The most common source for protein disorder annotations, DisProt, covers only a fraction of the available sequences. Alternatively, the Protein Data Bank (PDB) has been mined for missing residues in X-ray crystallographic structures. Herein, we provide a centralized source for data on different flavours of disorder in protein structures, MobiDB, building on and expanding the content provided by already existing sources. In addition to the DisProt and PDB X-ray structures, we have added experimental information from NMR structures and five different flavours of two disorder predictors (ESpritz and IUpred). These are combined into a weighted consensus disorder used to classify disordered regions into flexible and constrained disorder. Users are encouraged to submit manual annotations through a submission form. MobiDB features experimental annotations for 17 285 proteins, covering the entire PDB and predictions for the SwissProt database, with 565 200 annotated sequences. Depending on the disorder flavour, 6-20% of the residues are predicted as disordered. © The Author 2012. Published by Oxford University Press. All rights reserved.",
                        "date": "2012-08-01T00:00:00Z",
                        "citationCount": 110,
                        "authors": [
                            {
                                "name": "Di domenico T."
                            },
                            {
                                "name": "Walsh I."
                            },
                            {
                                "name": "Martin A.J.M."
                            },
                            {
                                "name": "Tosatto S.C.E."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/nar/gkx1071",
                    "pmid": "29136219",
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": "3",
                    "note": null,
                    "metadata": {
                        "title": "MobiDB 3.0: More annotations for intrinsic disorder, conformational diversity and interactions in proteins",
                        "abstract": "© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.The MobiDB (URL: mobidb.bio.unipd.it) database of protein disorder and mobility annotations has been significantly updated and upgraded since its last major renewal in 2014. Several curated datasets for intrinsic disorder and folding upon binding have been integrated from specialized databases. The indirect evidence has also been expanded to better capture information available in the PDB, such as high temperature residues in X-ray structures and overall conformational diversity. Novel nuclear magnetic resonance chemical shift data provides an additional experimental information layer on conformational dynamics. Predictions have been expanded to provide new types of annotation on backbone rigidity, secondary structure preference and disordered binding regions. MobiDB 3.0 contains information for the complete UniProt protein set and synchronization has been improved by covering all UniParc sequences. An advanced search function allows the creation of a wide array of custom-made datasets for download and further analysis. A large amount of information and cross-links to more specialized databases are intended to make MobiDB the central resource for the scientific community working on protein intrinsic disorder and mobility.",
                        "date": "2018-01-01T00:00:00Z",
                        "citationCount": 109,
                        "authors": [
                            {
                                "name": "Piovesan D."
                            },
                            {
                                "name": "Tabaro F."
                            },
                            {
                                "name": "Paladin L."
                            },
                            {
                                "name": "Necci M."
                            },
                            {
                                "name": "Micetic I."
                            },
                            {
                                "name": "Camilloni C."
                            },
                            {
                                "name": "Davey N."
                            },
                            {
                                "name": "Dosztanyi Z."
                            },
                            {
                                "name": "Meszaros B."
                            },
                            {
                                "name": "Monzon A.M."
                            },
                            {
                                "name": "Parisi G."
                            },
                            {
                                "name": "Schad E."
                            },
                            {
                                "name": "Sormanni P."
                            },
                            {
                                "name": "Tompa P."
                            },
                            {
                                "name": "Vendruscolo M."
                            },
                            {
                                "name": "Vranken W.F."
                            },
                            {
                                "name": "Tosatto S.C.E."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkaa1058",
                    "pmid": "33237329",
                    "pmcid": "PMC7779018",
                    "type": [
                        "Primary"
                    ],
                    "version": "4",
                    "note": null,
                    "metadata": {
                        "title": "MobiDB: Intrinsically disordered proteins in 2021",
                        "abstract": "© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.The MobiDB database (URL: https://mobidb.org/) provides predictions and annotations for intrinsically disordered proteins. Here, we report recent developments implemented in MobiDB version 4, regarding the database format, with novel types of annotations and an improved update process. The new website includes a re-designed user interface, a more effective search engine and advanced API for programmatic access. The new database schema gives more flexibility for the users, as well as simplifying the maintenance and updates. In addition, the new entry page provides more visualisation tools including customizable feature viewer and graphs of the residue contact maps. MobiDB v4 annotates the binding modes of disordered proteins, whether they undergo disorder-to-order transitions or remain disordered in the bound state. In addition, disordered regions undergoing liquid-liquid phase separation or post-translational modifications are defined. The integrated information is presented in a simplified interface, which enables faster searches and allows large customized datasets to be downloaded in TSV, Fasta or JSON formats. An alternative advanced interface allows users to drill deeper into features of interest. A new statistics page provides information at database and proteome levels. The new MobiDB version presents state-of-the-art knowledge on disordered proteins and improves data accessibility for both computational and experimental users.",
                        "date": "2021-01-08T00:00:00Z",
                        "citationCount": 3,
                        "authors": [
                            {
                                "name": "Piovesan D."
                            },
                            {
                                "name": "Necci M."
                            },
                            {
                                "name": "Escobedo N."
                            },
                            {
                                "name": "Monzon A.M."
                            },
                            {
                                "name": "Hatos A."
                            },
                            {
                                "name": "Micetic I."
                            },
                            {
                                "name": "Quaglia F."
                            },
                            {
                                "name": "Paladin L."
                            },
                            {
                                "name": "Ramasamy P."
                            },
                            {
                                "name": "Dosztanyi Z."
                            },
                            {
                                "name": "Vranken W.F."
                            },
                            {
                                "name": "Davey N.E."
                            },
                            {
                                "name": "Parisi G."
                            },
                            {
                                "name": "Fuxreiter M."
                            },
                            {
                                "name": "Tosatto S.C.E."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "University of Padua, Department of Biomedical Sciences, BioComputing UP lab",
                    "email": null,
                    "url": "https://biocomputingup.it/",
                    "orcidid": null,
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                    "rorid": null,
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                },
                {
                    "name": "Silvio C.E. Tosatto",
                    "email": "silvio.tosatto@unipd.it",
                    "url": "https://biocomputingup.it/",
                    "orcidid": "https://orcid.org/0000-0003-4525-7793",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
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                {
                    "name": "Damiano Piovesan",
                    "email": null,
                    "url": null,
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                    "rorid": null,
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                    "note": null
                },
                {
                    "name": "Francesco Tabaro",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-7559-5585",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
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                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Lisanna Paladin",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-0011-9397",
                    "gridid": null,
                    "rorid": null,
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                    "note": null
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                    "name": "Marco Necci",
                    "email": null,
                    "url": null,
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                    "rorid": null,
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            "description": "Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions.",
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                    "note": null,
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                    "uri": "http://edamontology.org/topic_3169",
                    "term": "ChIP-seq"
                }
            ],
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            "publication": [
                {
                    "doi": "10.1186/bcr3664",
                    "pmid": "24886537",
                    "pmcid": "PMC4076632",
                    "type": [
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "VAV3 mediates resistance to breast cancer endocrine therapy",
                        "abstract": "Introduction: Endocrine therapies targeting cell proliferation and survival mediated by estrogen receptor α (ERα) are among the most effective systemic treatments for ERα-positive breast cancer. However, most tumors initially responsive to these therapies acquire resistance through mechanisms that involve ERα transcriptional regulatory plasticity. Herein we identify VAV3 as a critical component in this process.Methods: A cell-based chemical compound screen was carried out to identify therapeutic strategies against resistance to endocrine therapy. Binding to ERα was evaluated by molecular docking analyses, an agonist fluoligand assay and short hairpin (sh)RNA-mediated protein depletion. Microarray analyses were performed to identify altered gene expression. Western blot analysis of signaling and proliferation markers, and shRNA-mediated protein depletion in viability and clonogenic assays, were performed to delineate the role of VAV3. Genetic variation in VAV3 was assessed for association with the response to tamoxifen. Immunohistochemical analyses of VAV3 were carried out to determine its association with therapeutic response and different tumor markers. An analysis of gene expression association with drug sensitivity was carried out to identify a potential therapeutic approach based on differential VAV3 expression.Results: The compound YC-1 was found to comparatively reduce the viability of cell models of acquired resistance. This effect was probably not due to activation of its canonical target (soluble guanylyl cyclase), but instead was likely a result of binding to ERα. VAV3 was selectively reduced upon exposure to YC-1 or ERα depletion, and, accordingly, VAV3 depletion comparatively reduced the viability of cell models of acquired resistance. In the clinical scenario, germline variation in VAV3 was associated with the response to tamoxifen in Japanese breast cancer patients (rs10494071 combined P value = 8.4 × 10-4). The allele association combined with gene expression analyses indicated that low VAV3 expression predicts better clinical outcome. Conversely, high nuclear VAV3 expression in tumor cells was associated with poorer endocrine therapy response. Based on VAV3 expression levels and the response to erlotinib in cancer cell lines, targeting EGFR signaling may be a promising therapeutic strategy.Conclusions: This study proposes VAV3 as a biomarker and a rationale for its use as a signaling target to prevent and/or overcome resistance to endocrine therapy in breast cancer. © 2014 Aguilar et al.; licensee BioMed Central Ltd.",
                        "date": "2014-05-28T00:00:00Z",
                        "citationCount": 19,
                        "authors": [
                            {
                                "name": "Aguilar H."
                            },
                            {
                                "name": "Urruticoechea A."
                            },
                            {
                                "name": "Halonen P."
                            },
                            {
                                "name": "Kiyotani K."
                            },
                            {
                                "name": "Mushiroda T."
                            },
                            {
                                "name": "Barril X."
                            },
                            {
                                "name": "Serra-Musach J."
                            },
                            {
                                "name": "Islam A."
                            },
                            {
                                "name": "Caizzi L."
                            },
                            {
                                "name": "Di Croce L."
                            },
                            {
                                "name": "Nevedomskaya E."
                            },
                            {
                                "name": "Zwart W."
                            },
                            {
                                "name": "Bostner J."
                            },
                            {
                                "name": "Karlsson E."
                            },
                            {
                                "name": "Perez Tenorio G."
                            },
                            {
                                "name": "Fornander T."
                            },
                            {
                                "name": "Sgroi D.C."
                            },
                            {
                                "name": "Garcia-Mata R."
                            },
                            {
                                "name": "Jansen M.P.H.M."
                            },
                            {
                                "name": "Garcia N."
                            },
                            {
                                "name": "Bonifaci N."
                            },
                            {
                                "name": "Climent F."
                            },
                            {
                                "name": "Soler M.T."
                            },
                            {
                                "name": "Rodriguez-Vida A."
                            },
                            {
                                "name": "Gil M."
                            },
                            {
                                "name": "Brunet J."
                            },
                            {
                                "name": "Martrat G."
                            },
                            {
                                "name": "Gomez-Baldo L."
                            },
                            {
                                "name": "Extremera A.I."
                            },
                            {
                                "name": "Figueras A."
                            },
                            {
                                "name": "Balart J."
                            },
                            {
                                "name": "Clarke R."
                            },
                            {
                                "name": "Burnstein K.L."
                            },
                            {
                                "name": "Carlson K.E."
                            },
                            {
                                "name": "Katzenellenbogen J.A."
                            },
                            {
                                "name": "Vizoso M."
                            },
                            {
                                "name": "Esteller M."
                            },
                            {
                                "name": "Villanueva A."
                            },
                            {
                                "name": "Rodriguez-Pena A.B."
                            },
                            {
                                "name": "Bustelo X.R."
                            },
                            {
                                "name": "Nakamura Y."
                            },
                            {
                                "name": "Zembutsu H."
                            },
                            {
                                "name": "Stal O."
                            },
                            {
                                "name": "Beijersbergen R.L."
                            },
                            {
                                "name": "Pujana M.A."
                            }
                        ],
                        "journal": "Breast Cancer Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Rory Stark",
                    "email": "rory.stark@cruk.cam.ac.uk",
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        {
            "name": "M2aia",
            "description": "M²aia (MSI applications for interactive analysis in MITK) is a software tool enabling interactive signal processing and visualisation of mass spectrometry imaging (MSI) datasets. M²aia extends the open source Medical Imaging and Interaction Toolkit (MITK; https://www.mitk.org)  and provides powerful methods that the MSI community can adopt, exploit and improve further. In it’s current state, it is designed to enable multi-modal 2D registration and 3D MSI reconstruction.",
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                    "note": "NET­WORK PHARMACOLOGY FOR PRE­CI­SION MEDICINE, FACULTY OF MEDICINE, UNIVERSITY OF HELSINKI"
                },
                {
                    "url": "https://twitter.com/SynergyFinder",
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                        "abstract": "© The Author 2017. Published by Oxford University Press. All rights reserved.Summary: Rational design of drug combinations has become a promising strategy to tackle the drug sensitivity and resistance problem in cancer treatment. To systematically evaluate the preclinical significance of pairwise drug combinations, functional screening assays that probe combination effects in a dose-response matrix assay are commonly used. To facilitate the analysis of such drug combination experiments, we implemented a web application that uses key functions of Rpackage SynergyFinder, and provides not only the flexibility of using multiple synergy scoring models, but also a user-friendly interface for visualizing the drug combination landscapes in an interactive manner.",
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                                "name": "He L."
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                                "name": "Aittokallio T."
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                            {
                                "name": "Tang J."
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                        ],
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                        "title": "Methods for high-throughput drug combination screening and synergy scoring",
                        "abstract": "© 2018, Springer Science+Business Media LLC.Gene products or pathways that are aberrantly activated in cancer but not in normal tissue hold great promises for being effective and safe anticancer therapeutic targets. Many targeted drugs have entered clinical trials but so far showed limited efficacy mostly due to variability in treatment responses and often rapidly emerging resistance. Toward more effective treatment options, we will need multi-targeted drugs or drug combinations, which selectively inhibit the viability and growth of cancer cells and block distinct escape mechanisms for the cells to become resistant. Functional profiling of drug combinations requires careful experimental design and robust data analysis approaches. At the Institute for Molecular Medicine Finland (FIMM), we have developed an experimental-computational pipeline for high-throughput screening of drug combination effects in cancer cells. The integration of automated screening techniques with advanced synergy scoring tools allows for efficient and reliable detection of synergistic drug interactions within a specific window of concentrations, hence accelerating the identification of potential drug combinations for further confirmatory studies.",
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                                "name": "He L."
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                            {
                                "name": "Aittokallio T."
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                            {
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                            {
                                "name": "Brunk B.P."
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                        ],
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                        "title": "The TIGR Gene Indices: Analysis of gene transcipt sequences in highly sampled eukaryotic species",
                        "abstract": "While genome sequencing projects are advancing rapidly, EST sequencing and analysis remains a primary research tool for the identification and categorization of gene sequences in a wide variety of species and an important resource for annotation of genomic sequence. The TIGR Gene Indices (http:// www.tigr.org/tdb/tgi.shtml) are a collection of species-specific databases that use a highly refined protocol to analyze EST sequences in an attempt to identify the genes represented by that data and to provide additional information regarding those genes. Gene Indices are constructed by first clustering, then assembling EST and annotated gene sequences from GenBank for the targeted species. This process produces a set of unique, high-fidelity virtual transcripts, or Tentative Consensus (TC) sequences. The TC sequences can be used to provide putative genes with functional annotation, to link the transcripts to mapping and genomic sequence data, to provide links between orthologous and paralogous genes and as a resource for comparative sequence analysis.",
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                            {
                                "name": "Cho J."
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                            {
                                "name": "Lee D."
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                            {
                                "name": "Liang F."
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                            {
                                "name": "Holt I."
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                            {
                                "name": "Karamycheva S."
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                            {
                                "name": "Parvizi B."
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                            {
                                "name": "Pertea G."
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                            {
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                            {
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                            {
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