Local Sequence Information‐based Support Vector Machine to Classify Voltage‐gated Potassium Channels

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 po...

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Bibliographic Details
Published in:Acta biochimica et biophysica Sinica 2006-06, Vol.38 (6), p.363-371
Main Authors: LIU, Li‐Xia, LI, Meng‐Long, TAN, Fu‐Yuan, LU, Min‐Chun, WANG, Ke‐Long, GUO, Yan‐Zhi, WEN, Zhi‐Ning, JIANG, Lin
Format: Article
Language:English
Subjects:
Algorithms
Animals
Artificial Intelligence
classification
Computational Biology - methods
dipeptide composition
Humans
Models, Biological
Models, Statistical
Peptides - chemistry
Potassium Channels, Voltage-Gated - classification
Potassium Channels, Voltage-Gated - genetics
Reproducibility of Results
Sensitivity and Specificity
Sequence Alignment
Sequence Analysis, Protein - methods
support vector machine
transmembrane domain
voltage‐gated potassium channel
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Summary: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
ISSN:1672-9145
1745-7270
DOI:10.1111/j.1745-7270.2006.00177.x