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  "Title": "Mechanism-Aware Imputation",
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  "Authors@R": "c(person(given = \"Jonathan\",\nfamily = \"Dekermanjian\",\nrole = c(\"aut\", \"cre\"),\nemail = \"Jonathan.Dekermanjian@CUAnschutz.edu\"),\nperson(given = \"Elin\",\nfamily = \"Shaddox\",\nrole = c(\"aut\"),\nemail = \"Elin.Shaddox@CUAnschutz.edu\"),\nperson(given = \"Debmalya\",\nfamily = \"Nandy\",\nrole = c(\"aut\"),\nemail = \"Debmalya.Nandy@CUAnschutz.edu\"),\nperson(given = \"Debashis\",\nfamily = \"Ghosh\",\nrole = c(\"aut\"),\nemail = \"Debashis.Ghosh@CUAnschutz.edu\"),\nperson(given = \"Katerina\",\nfamily = \"Kechris\",\nrole = c(\"aut\"),\nemail = \"Katerina.Kechris@CUAnschutz.edu\"))",
  "Description": "A two-step approach to imputing missing data in\nmetabolomics. Step 1 uses a random forest classifier to\nclassify missing values as either Missing Completely at\nRandom/Missing At Random (MCAR/MAR) or Missing Not At Random\n(MNAR). MCAR/MAR are combined because it is often difficult to\ndistinguish these two missing types in metabolomics data. Step\n2 imputes the missing values based on the classified missing\nmechanisms, using the appropriate imputation algorithms.\nImputation algorithms tested and available for MCAR/MAR include\nBayesian Principal Component Analysis (BPCA), Multiple\nImputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and\nRandom Forest. Imputation algorithms tested and available for\nMNAR include nsKNN and a single imputation approach for\nimputation of metabolites where left-censoring is present.",
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