Name
Abstract | ||
|---|---|---|
The weighted degree kernel is a good means to predict the splice site. Its prediction performance is affected by positions in the DNA sequence of nucleotide bases. Based on this fact, we propose confusing positions in this article. Using the confusing positions and the key positions which we proposed in previous work, we construct a weight array to obtain adaptive weighted degree kernel, a kind of string kernel to predict the splice site. Then to prove the efficient and advance of the method, we use the public available dataset to train support vector machines to compare the performance of the adaptive weighted degree kernel and conventional weighted degree kernel. The results show that the adaptive weighted degree kernel has better performance than the weighted degree kernel. © Springer International Publishing AG 2016. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46654-5_81 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
Splice site prediction,Adaptive weighted degree kernel,Confusing positions,Weight array,Support vector machine | Kernel (linear algebra),Computer science,Support vector machine,Algorithm,RNA splicing,String kernel | Conference |
Volume | ISSN | Citations |
9967 LNCS | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wang Tianqi | 1 | 0 | 0.34 |
| Yan Ke | 2 | 0 | 0.68 |
| Xu Yong | 3 | 2119 | 73.51 |
| Liu Jin-Xing | 4 | 40 | 16.11 |