کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5513338 | 1541204 | 2017 | 11 صفحه PDF | دانلود رایگان |
- Supports various microRNA target prediction algorithms.
- Facilitates integrative analysis by the prediction overview and subset selection.
- Provides interactive visualizations for the microRNA target prediction results.
- Reports results of two case studies using human miRNA-mRNA expression profile data.
In this paper, we present miRTarVis+, a Web-based interactive visual analytics tool for miRNA target predictions and integrative analyses of multiple prediction results. Various microRNA (miRNA) target prediction algorithms have been developed to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. There are also a few analytics tools to help researchers predict targets of miRNAs. However, there still is a need for improving the performance for miRNA prediction algorithms and more importantly for interactive visualization tools for an integrative analysis of multiple prediction results. miRTarVis+ has an intuitive interface to support the analysis pipeline of load, filter, predict, and visualize. It can predict targets of miRNA by adopting Bayesian inference and maximal information-based nonparametric exploration (MINE) analyses as well as conventional correlation and mutual information analyses. miRTarVis+ supports an integrative analysis of multiple prediction results by providing an overview of multiple prediction results and then allowing users to examine a selected miRNA-mRNA network in an interactive treemap and node-link diagram. To evaluate the effectiveness of miRTarVis+, we conducted two case studies using miRNA-mRNA expression profile data of asthma and breast cancer patients and demonstrated that miRTarVis+ helps users more comprehensively analyze targets of miRNA from miRNA-mRNA expression profile data. miRTarVis+ is available at http://hcil.snu.ac.kr/research/mirtarvisplus.
Journal: Methods - Volume 124, 15 July 2017, Pages 78-88