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- Title
Local Sequence Information-based Support Vector Machine to Classify Voltage-gated Potassium Channels.
- Authors
Li-Xia Liu; Meng-Long Li; Fu-Yuan Tan; Min-Chun Lu; Ke-Long Wang; Yan-Zhi Guo; Zhi-Ning Wen; Lin Jiang
- Abstract
In our previous work, we developed a computational tool, PreK-ClassK-ClassKv, to predict and classify potassium (K+) channels. For K+ channel prediction (PreK) and classification at family level (ClassK), this method performs well. However, it does not perform so well in classifying voltage-gated potassium (Kv) channels (ClassKv). In this paper, a new method based on the local sequence information of Kv channels is introduced to classify Kv channels. Six transmembrane domains of a Kv channel protein are used to define a protein, and the dipeptide composition technique is used to transform an amino acid sequence to a numerical sequence. A Kv channel protein is represented by a vector with 2000 elements, and a support vector machine algorithm is applied to classify Kv channels. This method shows good performance with averages of total accuracy (Acc), sensitivity (SE), specificity (SP), reliability (R) and Matthews correlation coefficient (MCC) of 98.0%, 89.9%, 100%, 0.95 and 0.94 respectively. The results indicate that the local sequence information-based method is better than the global sequence information-based method to classify Kv channels. Edited by Juan LIU
- Subjects
POTASSIUM channels; ION channels; PROTEIN analysis; AMINO acid sequence; MATHEMATICAL sequences; ALGORITHMS
- Publication
Acta Biochimica et Biophysica Sinica, 2006, Vol 38, Issue 6, p363
- ISSN
1672-9145
- Publication type
Article
- DOI
10.1111/j.1745-7270.2006.00177.x