Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA.
Nawgen LLC, St. Louis, MO, USA.
Genome Biol. 2019 Jan 22;20(1):18. doi: 10.1186/s13059-019-1629-z.
We perform a large-scale RNA sequencing study to experimentally identify genes that are downregulated by 25 miRNAs. This RNA-seq dataset is combined with public miRNA target binding data to systematically identify miRNA targeting features that are characteristic of both miRNA binding and target downregulation. By integrating these common features in a machine learning framework, we develop and validate an improved computational model for genome-wide miRNA target prediction. All prediction data can be accessed at miRDB ( http://mirdb.org ).
我们进行了一项大规模的 RNA 测序研究，以实验鉴定被 25 种 miRNA 下调的基因。该 RNA-seq 数据集与公共 miRNA 靶标结合数据相结合，系统地鉴定了 miRNA 靶标特征，这些特征既与 miRNA 结合又与靶标下调有关。通过在机器学习框架中整合这些共同特征，我们开发并验证了一个用于全基因组 miRNA 靶标预测的改进计算模型。所有预测数据均可在 miRDB（http://mirdb.org）上获取。
