Name
Abstract | ||
|---|---|---|
Although thousands of microRNAs (miRNAs) have been identified in recent experimental efforts, it remains a challenge to explore their specific biological functions through molecular biological experiments. Since those members from same family share same or similar biological functions, classifying new miRNAs into their corresponding families will be helpful for their further functional analysis. In this study, we initially built a vector space by characterizing the features from miRNA sequences and structures according to their miRBase family organizations. Then we further assigned miRNAs into its specific miRNA families by developing a novel genes discriminant analysis (GDA) approach in this study. As can be seen from the results of new families from GDA, in each of these new families, there was a high degree of similarity among all members of nucleotide sequences. At the same time, we employed 10-fold cross-validation machine learning to achieve the accuracy rates of 68.68%, 80.74%, and 83.65% respectively for the original miRNA families with no less than two, three, and four members. The encouraging results suggested that the proposed GDA could not only provide a support in identifying new miRNAs’ families, but also contributing to predicting their biological functions. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.compbiolchem.2017.09.008 | Computational Biology and Chemistry |
Keywords | Field | DocType |
microRNA,Family classification,Genes discriminant analysis,10-fold cross-validation | Functional analysis,Degree of similarity,Gene,Biology,microRNA,MiRBase,Bioinformatics,Linear discriminant analysis,Genetics | Journal |
Volume | Issue | ISSN |
71 | C | 1476-9271 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Ding | 1 | 15 | 8.48 |
| Junhua Xu | 2 | 0 | 1.01 |
| Mengmeng Sun | 3 | 12 | 2.30 |
| Shanshan Zhu | 4 | 0 | 1.35 |
| Jie Gao | 5 | 2174 | 155.61 |