Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data

By Weijun Liu, Xiaowei Wang. Genome Biology, 2019

738 citations recorded by the source corpus.

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).

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fc32074b37a6d9dda535a70f9689022e70508520

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Yuhao Chen
2. Xiaowei Wang
3. Jiuyong Li
4. Murray J. Cairns
5. Bing Liu

In Molecular Biology

1. Yuhao Chen
2. Xiaowei Wang
3. Murray J. Cairns
4. Jiuyong Li
5. Bing Liu

Citations in the corpus

• miRDB: an online database for prediction of functional microRNA targets (2019, methodology): “Comparative analysis using independent datasets indicates that Mir-Target has improved performance over other existing prediction algorithms (9).”