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000124256 1001_ $$0P:(DE-He78)c226b166c5ffe671658fc70605a17602$$aGaluschka, Claudia$$b0$$eFirst author$$udkfz
000124256 245__ $$aModels in Translational Oncology: A Public Resource Database for Preclinical Cancer Research.
000124256 260__ $$aPhiladelphia, Pa.$$bAACR$$c2017
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000124256 520__ $$aThe devastating diseases of human cancer are mimicked in basic and translational cancer research by a steadily increasing number of tumor models, a situation requiring a platform with standardized reports to share model data. Models in Translational Oncology (MiTO) database was developed as a unique Web platform aiming for a comprehensive overview of preclinical models covering genetically engineered organisms, models of transplantation, chemical/physical induction, or spontaneous development, reviewed here. MiTO serves data entry for metastasis profiles and interventions. Moreover, cell lines and animal lines including tool strains can be recorded. Hyperlinks for connection with other databases and file uploads as supplementary information are supported. Several communication tools are offered to facilitate exchange of information. Notably, intellectual property can be protected prior to publication by inventor-defined accessibility of any given model. Data recall is via a highly configurable keyword search. Genome editing is expected to result in changes of the spectrum of model organisms, a reason to open MiTO for species-independent data. Registered users may deposit own model fact sheets (FS). MiTO experts check them for plausibility. Independently, manually curated FS are provided to principle investigators for revision and publication. Importantly, noneditable versions of reviewed FS can be cited in peer-reviewed journals. Cancer Res; 77(10); 2557-63. ©2017 AACR.
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000124256 7001_ $$0P:(DE-He78)c0313b77e0c44cd2f5eb85b747c88be0$$aProynova, Rumyana$$b1$$udkfz
000124256 7001_ $$0P:(DE-He78)535b205a5a11ddcc83a9301bd41a83f1$$aRoth, Benjamin$$b2$$udkfz
000124256 7001_ $$0P:(DE-He78)2e92d0ae281932fc7347d819fec36b0b$$aAugustin, Hellmut$$b3$$udkfz
000124256 7001_ $$0P:(DE-He78)799d978330dff449f8244947929a4518$$aMüller-Decker, Karin$$b4$$eLast author$$udkfz
000124256 773__ $$0PERI:(DE-600)2036785-5$$a10.1158/0008-5472.CAN-16-3099$$gVol. 77, no. 10, p. 2557 - 2563$$n10$$p2557 - 2563$$tCancer research$$v77$$x1538-7445$$y2017
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