Name
Title | ||
|---|---|---|
Predicting human miRNA target genes using a novel computational intelligent framework. |
Abstract | ||
|---|---|---|
The discovery of miRNAs had a great impact on traditional biology. Typically, miRNAs have the potential to bind to the 3′untranslated region (UTR) of their mRNA target genes for cleavage or translational repression. The experimental identification of their targets has many drawbacks including cost, time and low specificity and these are the reasons why many computational approaches have been developed so far. However, existing computational approaches do not include any advanced feature selection technique and they are facing problems concerning their classification performance and their interpretability. In the present paper, we propose a computational intelligent framework to overcome existing difficulties in predicting miRNA targets. This framework includes three distinct steps: a filtering step, a novel hybrid classification methodology and an advanced methodology to extract interpretable fuzzy rules from the final prediction model. The classification methodology relies on a combination of Genetic Algorithms and Support Vector Machines in order to locate the optimal feature subset while achieving high classification performance. The proposed methodology has been compared with several existing methodologies, a very popular artificial neural network based classification technique and a traditional fuzzy rule based classification method. It was proved that the new method outperforms them in terms of classification performance while it selects a much smaller feature subset and it is able to uncover meaningful biological conclusions. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.ins.2014.09.016 | Information Sciences |
Keywords | Field | DocType |
miRNAs,miRNA targets,Genetic Algorithm,Evolutionary computation,Support Vector Machines,Fuzzy logic | Interpretability,Data mining,Feature selection,Computer science,Support vector machine,Fuzzy logic,Evolutionary computation,Artificial intelligence,Artificial neural network,Genetic algorithm,Machine learning,Fuzzy rule | Journal |
Volume | ISSN | Citations |
294 | 0020-0255 | 1 |
PageRank | References | Authors |
0.35 | 20 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aigli Korfiati | 1 | 18 | 3.28 |
| Konstantinos A. Theofilatos | 2 | 99 | 13.69 |
| Dimitris Kleftogiannis | 3 | 35 | 3.97 |
| Christos E. Alexakos | 4 | 17 | 9.87 |
| Spiros Likothanassis | 5 | 84 | 12.44 |
| Seferina Mavroudi | 6 | 73 | 14.58 |