iCTX-Type statistics

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Citations per year

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Associated diseases

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iCTX-Type specifications


Unique identifier OMICS_18417
Name iCTX-Type
Interface Web user interface
Restrictions to use None
Input data A query protein sequences.
Input format FASTA
Computer skills Basic
Stability Stable
Maintained Yes


  • person_outline Hao Lin <>

Publication for iCTX-Type

iCTX-Type in publications

PMCID: 5401747
PMID: 28497044
DOI: 10.1155/2017/2929807

[…] there are some studies on the prediction of ion channel types of conotoxins. the contrast experiments were shown in ., it can be seen from that avc-svm model is better than the bidi-rbf model and ictx-type model in terms of average accuracy, overall accuracy, and time efficiency. when compared with f-score-svm, the average accuracy and the overall accuracy of the avc-svm model are not as high […]

PMCID: 5008028
PMID: 27631006
DOI: 10.1155/2016/3981478

[…] and 95.6%, respectively, which are higher than those of rbf network-based method []. the sns of k- and ca-conotoxins of our method are 91.7% and 95.6%, respectively, which are higher than those of ictx-type []. thus, in summary, our proposed method is superior to other published methods., in this paper, we designed a new method based on three kinds of new properties to predict three kinds […]

PMCID: 4421104
PMID: 25977917
DOI: 10.1155/2015/184824

[…] conotoxins are small disulfide-rich neurotoxic peptides, which can bind to ion channels with very high specificity and regulate their activities. h. ding et al. developed a novel method called ictx-type, which is a sequence-based predictor that can be used to identify the types of conotoxins in targeting ion channels. a user-friendly web tool is also available. y.-z. zhou et al. analyzed […]

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iCTX-Type institution(s)
Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, China; Gordon Life Science Institute, Boston, MA, USA; Department of Physics, School of Sciences Center for Genomics and Computational Biology, Hebei United University, Tangshan, China; Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
iCTX-Type funding source(s)
Supported by the National Nature Scientific Foundation of China (nos. 61202256, 61301260, and 61100092), the Nature Scientific Foundation of Hebei Province (no. C2013209105), and the Fundamental Research Funds for the Central Universities (nos. ZYGX2012J113 and ZYGX2013J102).

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