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    "description": "Dataset GSE50161 on brain cancer gene expression from CuMiDa\n\n5 classes\n54676 genes\n130 samples\n\nAbout\nHere we present the Curated Microarray Database (CuMiDa), a repository containing 78 handpicked cancer microarray datasets, extensively curated from 30.000 studies from the Gene Expression Omnibus (GEO), solely for machine learning. The aim of CuMiDa is to offer homogeneous and state-of-the-art biological preprocessing of these datasets, together with numerous 3-fold cross validation benchmark results to propel machine learning studies focused on cancer research. The database make available various download options to be employed by other programs, as well for PCA and t-SNE results. CuMiDa stands different from existing databases for offering newer datasets, manually and carefully curated, from samples quality, unwanted probes, background correction and normalization, to create a more reliable source of data for computational research.\nhttp:\/\/sbcb.inf.ufrgs.br\/cumida\n\nReferences\n\nFeltes, B.C.; Chandelier, E.B.; Grisci, B.I.; Dorn, M. (2019) CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research. Journal of Computational Biology, 26 (4), 376-386. [https:\/\/doi.org\/10.1089\/cmb.2018.0238]\nGrisci, B. I., Feltes, B. C.,  Dorn, M. (2019). Neuroevolution as a tool for microarray gene expression pattern identification in cancer research. Journal of biomedical informatics, 89, 122-133. [https:\/\/doi.org\/10.1016\/j.jbi.2018.11.013]\n\nInspiration\n\nHow to deal with class imbalance for classification?\nHow to identify the most important genes for the classification of each cancer subtype?\nIs it possible to discover subtypes?\nHow to beat the classification and clustering benchmarks for this dataset listed on the CuMiDa website?",
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