Name
Abstract | ||
|---|---|---|
MicroRNAs (miRNAs) are involved in many diverse biological processes and they may potentially regulate the functions of thousands of genes. However, one major issue in miRNA studies is the lack of bioinformatics programs to accurately predict miRNA targets. Animal miRNAs have limited sequence complementarity to their gene targets, which makes it challenging to build target prediction models with high specificity.Here we present a new miRNA target prediction program based on support vector machines (SVMs) and a large microarray training dataset. By systematically analyzing public microarray data, we have identified statistically significant features that are important to target downregulation. Heterogeneous prediction features have been non-linearly integrated in an SVM machine learning framework for the training of our target prediction model, MirTarget2. About half of the predicted miRNA target sites in human are not conserved in other organisms. Our prediction algorithm has been validated with independent experimental data for its improved performance on predicting a large number of miRNA down-regulated gene targets.All the predicted targets were imported into an online database miRDB, which is freely accessible at http://mirdb.org. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/bioinformatics/btm595 | Bioinformatics |
Keywords | Field | DocType |
new mirna target prediction,target prediction model,mirna study,heterogeneous prediction feature,mirna target site,gene target,nonconserved microrna target,independent experimental data,mirna target,prediction algorithm,mirna down-regulated gene target,prediction model,statistical significance,machine learning,support vector machine,gene targeting,biological process,microrna,microarray data | Data mining,Gene silencing,Online database,Computer science,Support vector machine,microRNA,Microarray analysis techniques,Bioinformatics,RNA interference | Journal |
Volume | Issue | ISSN |
24 | 3 | 1367-4811 |
Citations | PageRank | References |
52 | 4.42 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao-Wei Wang | 1 | 596 | 59.78 |
| Issam El-Naqa | 2 | 528 | 36.31 |