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Predictor from Sequence of ION channels plus BLAST PSIONplus

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Predicts ion channels proteins and their types, and subtypes of the voltage-gated ion channels. Empirical results show that combination of results generated by SVM model with the alignment by BLAST that is implemented in PSIONplus leads to improved predictive performance for the prediction of ion channels and voltage-gated channel subtypes when compared to using just BLAST. Results on the benchmark datasets that are independent of the datasets used to design our predictor reveal that PSIONplus obtains relatively good predictive performance. Its accuracy is 85.4% for the prediction of ion channels, 68.3% for the prediction of ion channel types, and its average accuracy is 96.4% for the prediction of the four subtypes of the voltage-gated channels.

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PSIONplus classification

PSIONplus specifications

Software type:
Application/Script
Restrictions to use:
None
Computer skills:
Advanced
Maintained:
Yes
Interface:
Command line interface
Operating system:
Unix/Linux
Stability:
Stable

PSIONplus distribution

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No versioning.

PSIONplus support

Maintainer

  • Lukasz Kurgan <>

Credits

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Publications

Institution(s)

School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China; Department of Statistics, University of California Riverside, Riverside, CA, USA; Graduate School at Shenzhen, Tsinghua University, Shenzhen, China; State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA

Funding source(s)

The National Science Foundation of China (NSFC) grants 31050110432 and 31150110577; the Discovery grant 298328 from National Science and Engineering Research Council (NSERC) Canada; the International Development Research Center, Ottawa, Canada grant 104519-010; Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) grant 20130031120001; the National Science Foundation of China (NSFC) grant 11101226

Link to literature

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