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  "Title": "Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples",
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  "Author": "Cristian Yones",
  "Maintainer": "Cristian Yones <cyones@sinc.unl.edu.ar>",
  "Description": "Machine learning method specifically designed for\npre-miRNA prediction. It takes advantage of unlabeled sequences\nto improve the prediction rates even when there are just a few\npositive examples, when the negative examples are unreliable or\nare not good representatives of its class. Furthermore, the\nmethod can automatically search for negative examples if the\nuser is unable to provide them. MiRNAss can find a good\nboundary to divide the pre-miRNAs from other groups of\nsequences; it automatically optimizes the threshold that\ndefines the classes boundaries, and thus, it is robust to high\nclass imbalance. Each step of the method is scalable and can\nhandle large volumes of data.",
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