Issue No. 02 - March-April (2013 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.5
Qingfeng Chen , State Key Lab. for Conservation & Utilization of Subtropical Agro-bioresources, Guangxi Univ., Nanning, China
Wei Lan , Sch. of Comput., Electron. & Inf., Guangxi Univ., Nanning, China
Jianxin Wang , Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China
MicroRNA (miRNA) is an endogenous small noncoding RNA that plays an important role in gene expression through the post-transcriptional gene regulation pathways. There are many literature works focusing on predicting miRNA targets and exploring gene regulatory networks of miRNA families. We suggest, however, the study to identify the interaction between miRNAs is insufficient. This paper presents a framework to identify relationships between miRNAs using joint entropy, to investigate the regulatory features of miRNAs. Both the sequence and secondary structure are taken into consideration to make our method more relevant from the biological viewpoint. Further, joint entropy is applied to identify correlated miRNAs, which are more desirable from the perspective of the gene regulatory network. A data set including Drosophila melanogaster and Anopheles gambiae is used in the experiment. The results demonstrate that our approach is able to identify known miRNA interaction and uncover novel patterns of miRNA regulatory network.
Entropy, Joints, RNA, Educational institutions, Databases, Computational biology,joint entropy, Entropy, Joints, RNA, Educational institutions, Databases, Computational biology, interaction, Structure, similarity, miRNA
Qingfeng Chen, Wei Lan, Jianxin Wang, "Mining Featured Patterns of MiRNA Interaction Based on Sequence and Structure Similarity", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. , pp. 415-422, March-April 2013, doi:10.1109/TCBB.2013.5