The genome-wide discovery of microRNAs (miRNAs) involves identifying sequences having the highest chance of being a novel miRNA precursor (pre-miRNA), within all the possible sequences in a complete genome. The known pre-miRNAs are usually just a few in comparison to the millions of candidates that have to be analyzed. This is of particular interest in non-model species and recently sequenced genomes, where the challenge is to find potential pre-miRNAs only from the sequenced genome. The task is unfeasible without the help of computational methods, such as deep learning. However, it is still very difficult to find an accurate predictor, with a low false positive rate in this genome-wide context. Although there are many available tools, these have not been tested in realistic conditions, with sequences from whole genomes and the high class imbalance inherent to such data.

中文翻译：

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pre-miRNA 的全基因组发现：基于机器学习的最新方法的比较。

microRNA (miRNA) 的全基因组发现涉及在完整基因组中的所有可能序列中识别最有可能成为新 miRNA 前体 (pre-miRNA) 的序列。与必须分析的数百万个候选物相比，已知的 pre-miRNAs 通常只是少数。这对非模型物种和最近测序的基因组特别感兴趣，其中的挑战是仅从测序的基因组中找到潜在的 pre-miRNA。如果没有深度学习等计算方法的帮助，这项任务是不可行的。然而，在这种全基因组的背景下，仍然很难找到一个准确的预测因子，假阳性率很低。虽然有很多可用的工具，但这些都没有在现实条件下进行过测试，