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| Content Provider | ACM Digital Library |
|---|---|
| Author | Retsi, Vasiliki Leonti, Ianthi Aggeliki Mavroudi, Seferina Korfiati, Aigli Theofilatos, Konstantinos Likothanassis, Spiros |
| Abstract | High throughput sequencing technologies alongside with advanced bioinformatics methods have uncovered a vast number of short non-coding RNAs. They are split in various categories based on their cellular functionality and their sequential, thermodynamic and structural properties. Existing computational methods emphasize on the prediction of only one type of non-coding RNAs and thus their applicability in full transcriptome studies is limited. Only a few methods so far have been proposed for predicting multiple short non-coding RNAs and they do not cover the most significant classes of them. In the present paper, we introduce a new multiclass method based on the combination of genetic algorithms and support vector machines which is able to distinguish among tRNAs, miRNAs, snoRNAs, rRNAs and other RNA sequences with accuracy over 93%. Finally, the advanced feature selection mechanism of the proposed method uncovers significant characteristics for each one of the studied short non-coding RNAs. |
| Starting Page | 1 |
| Ending Page | 9 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781450335805 |
| DOI | 10.1145/2797143.2797179 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-09-25 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Short non-coding rnas classification Feature selection Hybrid methods Multi-classification system One versus all multi-classification approach. |
| Content Type | Text |
| Resource Type | Article |
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