ArticleInternational journal of molecular sciences2014
Identifying the subfamilies of voltage-gated potassium channels using feature selection technique.
Article in International journal of molecular sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- GPT2-ICC: A data-driven approach for accurate ion channel identification using pre-trained large language models.Journal of pharmaceutical analysis · 2025Article
- Review
- PSIONplusBiomolecules · 2020Article
- Special Protein Molecules Computational Identification.International journal of molecular sciences · 2018Article
- SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides.BMC genomics · 2017Article
- IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types.International journal of molecular sciences · 2017Article
- Pipeline for the identification and classification of ion channels in parasitic flatworms.Parasites & vectors · 2016Article
- BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species.BioMed research international · 2016Article
- Article
- Protein Remote Homology Detection Based on an Ensemble Learning Approach.BioMed research international · 2016Article
- Predicting cancerlectins by the optimal g-gap dipeptides.Scientific reports · 2015Article
- Briefing in application of machine learning methods in ion channel prediction.TheScientificWorldJournal · 2015Review
- Survey of Natural Language Processing Techniques in Bioinformatics.Computational and mathematical methods in medicine · 2015Review
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Voltage-gated K+ channel (VKC) plays important roles in biology procession, especially in nervous system. Different subfamilies of VKCs have different biological functions. Thus, knowing VKCs' subfamilies has become a meaningful job because it can guide the direction for the disease diagnosis and drug design. However, the traditional wet-experimental methods were costly and time-consuming. It is highly desirable to develop an effective and powerful computational tool for identifying different subfamilies of VKCs. In this study, a predictor, called iVKC-OTC, has been developed by incorporating the optimized tripeptide composition (OTC) generated by feature selection technique into the general form of pseudo-amino acid composition to identify six subfamilies of VKCs. One of the remarkable advantages of introducing the optimized tripeptide composition is being able to avoid the notorious dimension disaster or over fitting problems in statistical predictions. It was observed on a benchmark dataset, by using a jackknife test, that the overall accuracy achieved by iVKC-OTC reaches to 96.77% in identifying the six subfamilies of VKCs, indicating that the new predictor is promising or at least may become a complementary tool to the existing methods in this area. It has not escaped our notice that the optimized tripeptide composition can also be used to investigate other protein classification problems.
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