Name
Title | ||
|---|---|---|
A novel approach to estimation of E. coli promoter gene sequences: Combining feature selection and least square support vector machine (FS_LSSVM) |
Abstract | ||
|---|---|---|
In this paper, we have investigated the real-world task of recognizing biological concepts in DNA sequences. Recognizing promoters in strings that represent nucleotides (one of A, G, T, or C) has been performed using a novel approach based on combining feature selection (FS) and least square support vector machine (LSSVM). Dimensionality of Escherichia coli promoter gene sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 non-promoters. The proposed system consists of two parts. Firstly, we have used the FS process to reduce the dimensionality of E. coli promoter gene sequences dataset that has 57 attributes. So the dimensionality of this dataset has been reduced to 4 attributes by means of FS process. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.amc.2007.02.033 | Applied Mathematics and Computation |
Keywords | Field | DocType |
E. coli promoter gene sequences,Feature selection,LSSVM classifier,Estimation | Least squares,Promoter,Confusion matrix,Feature selection,Artificial intelligence,Classifier (linguistics),Mathematical optimization,Pattern recognition,Support vector machine,Curse of dimensionality,Cross-validation,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
190 | 2 | 0096-3003 |
Citations | PageRank | References |
8 | 0.73 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kemal Polat | 1 | 1348 | 97.38 |
| Salih Güneş | 2 | 1267 | 78.53 |