Defining the Importance Score of Human MicroRNAs and Their Single Nucleotide Mutants Using Random Forest Regression and Sequence Data. Issue 9 (15th July 2019) - Cui, Chunmei; Shi, Bing; Shi, Jiangcheng; Zhou, Yuan; Cui, Qinghua
Abstract: To dig critical microRNAs (miRNAs) from the big miRNAome, it is becoming emergent to quantify the importance of miRNAs. However, computational methods for this purpose are still not available. Some sequence features associated with miRNA conservation are revealed and miRNA disease spectrum width (DSW) is defined as a score to measure the importance of miRNAs to some extent. Here, a random forest based regression model, microRNA importance calculator (MIC), is proposed to estimate the relationship between miRNA sequence and DSW score. The result shows that MIC score fits DSW score very well. Moreover, the MIC score significantly correlates with some established biological metrics for miRNA importance, such as miRNA conservation and expression level. Finally, MIC is extended to explore miRNAs with different importance scores across species for evaluating the possibility of druggable miRNAs and estimate how single nucleotide mutants affect the importance of miRNAs. MIC is available athttp://www.cuilab.cn/mic . Abstract : To quantify microRNAs (miRNAs) importance and essentiality, a model only using miRNA sequence information is proposed. The model has a good prediction performance and fine application prospects. The model can be used to evaluate the possibility of druggble miRNAs. It is also discussed how single nucleotide mutants affect the importance of miRNAs.