Yanqiu Wang

Publications (4)11.76 Total impact

• Article: Dissection of human MiRNA regulatory influence to subpathway.

  Xia Li, Wei Jiang, Wei Li, Baofeng Lian, Shuyuan Wang, Mingzhi Liao, Xiaowen Chen, Yanqiu Wang, Yingli Lv, Shiyuan Wang, Lei Yang
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  ABSTRACT: The global insight into the relationships between miRNAs and their regulatory influences remains poorly understood. And most of complex diseases may be attributed to certain local areas of pathway (subpathway) instead of the entire pathway. Here, we reviewed the studies on miRNA regulations to pathways and constructed a bipartite miRNAs and subpathways network for systematic analyzing the miRNA regulatory influences to subpathways. We found that a small fraction of miRNAs were global regulators, environmental information processing pathways were preferentially regulated by miRNAs, and miRNAs had synergistic effect on regulating group of subpathways with similar function. Integrating the disease states of miRNAs, we also found that disease miRNAs regulated more subpathways than nondisease miRNAs, and for all miRNAs, the number of regulated subpathways was not in proportion to the number of the related diseases. Therefore, the study not only provided a global view on the relationships among disease, miRNA and subpathway, but also uncovered the function aspects of miRNA regulations and potential pathogenesis of complex diseases. A web server to query, visualize and download for all the data can be freely accessed at http://bioinfo.hrbmu.edu.cn/miR2Subpath.
  Briefings in Bioinformatics 09/2011; 13(2):175-86. · 5.20 Impact Factor
• Article: Predicting human microRNA precursors based on an optimized feature subset generated by GA-SVM.

  Yanqiu Wang, Xiaowen Chen, Wei Jiang, Li Li, Wei Li, Lei Yang, Mingzhi Liao, Baofeng Lian, Yingli Lv, Shiyuan Wang, Shuyuan Wang, Xia Li
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  ABSTRACT: MicroRNAs (miRNAs) are non-coding RNAs that play important roles in post-transcriptional regulation. Identification of miRNAs is crucial to understanding their biological mechanism. Recently, machine-learning approaches have been employed to predict miRNA precursors (pre-miRNAs). However, features used are divergent and consequently induce different performance. Thus, feature selection is critical for pre-miRNA prediction. We generated an optimized feature subset including 13 features using a hybrid of genetic algorithm and support vector machine (GA-SVM). Based on SVM, the classification performance of the optimized feature subset is much higher than that of the two feature sets used in microPred and miPred by five-fold cross-validation. Finally, we constructed the classifier miR-SF to predict the most recently identified human pre-miRNAs in miRBase (version 16). Compared with microPred and miPred, miR-SF achieved much higher classification performance. Accuracies were 93.97%, 86.21% and 64.66% for miR-SF, microPred and miPred, respectively. Thus, miR-SF is effective for identifying pre-miRNAs.
  Genomics 05/2011; 98(2):73-8. · 3.02 Impact Factor
• Article: Systematic analysis of regulation and functions of co-expressed microRNAs in humans.

  Mingzhi Liao, Wei Jiang, Xiaowen Chen, Baofeng Lian, Wei Li, Yingli Lv, Yanqiu Wang, Shiyuan Wang, Xia Li
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  ABSTRACT: MicroRNAs (miRNAs) are a class of small non-coding RNA genes that post-transcriptionally regulate gene expression. With the development of high-throughput miRNA detection technology, researchers have begun to investigate the relationships between miRNA expression and its functions. In this study, we systematically analyzed the underlying molecular mechanisms and biological functions of co-expressed miRNAs. By integrating miRNA expression profiles, miRNA genome locations, transcriptional factors (TFs) of miRNAs and their target genes, we concluded that co-expressed miRNAs are more likely to be located in the same miRNA cluster (p = 6.05 x 10(-30)), are regulated by more common TFs (p = 9.17 x 10(-17)) and have consistent functions (p = 1.01 x 10(-6)). Moreover, among the top 10% (84) co-expressed miRNA pairs that are located on the same chromosomes, 37 miRNA pairs are located in the same cluster. Of the remaining 47 pairs, 36 miRNA pairs share more common TFs (>7). They account for 73/84 (86.9%) of total miRNA pairs. Finally, we further analyzed the top 10 co-expressed miRNA pairs. Almost all of these miRNA pairs are located in the same cluster, are regulated by many common TFs and have highly consistent functions, in agreement with previous reports. Thus, our study may provide an important reference for miRNA regulations and functions.
  Molecular BioSystems 10/2010; 6(10):1863-72. · 3.53 Impact Factor
• Conference Proceeding: A Novel Ensemble Decision Tree Approach for Mining Genes Coding Ion Channels for Cardiopathy Subtype.

  Jie Zhang, Xia Li, Wei Jiang, Yanqiu Wang, Chuanxing Li, Qiuju Wang, Shaoqi Rao
  Fuzzy Systems and Knowledge Discovery, Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II; 01/2005