Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.

Wang Xianfang, Wang Junmei, Wang Xiaolei, Zhang Yue

Abstract read

In one paragraph

Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it

0cells of the map it votes in

8citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning.ACS chemical neuroscience · 2025
Article
Conotoxins: Classification, Prediction, and Future Directions in Bioinformatics.Toxins · 2025
Review
In Silico Conotoxin Studies: Progress and Prospects.Molecules (Basel, Switzerland) · 2024
Review
Machine Learning Framework for Conotoxin Class and Molecular Target Prediction.Toxins · 2024
Article
Conotoxin Prediction: New Features to Increase Prediction Accuracy.Toxins · 2023
Article
Snails In Silico: A Review of Computational Studies on the Conopeptides.Marine drugs · 2019
Review
Classes, Databases, and Prediction Methods of Pharmaceutically and Commercially Important Cystine-Stabilized Peptides.Toxins · 2018
Review
Recent Advances in Conotoxin Classification by Using Machine Learning Methods.Molecules (Basel, Switzerland) · 2017
Review

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Wang XianfangSchool of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.ORCID 0000-0001-8355-1470

Wang JunmeiSchool of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.ORCID 0000-0002-0804-4281

Wang XiaoleiSchool of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.

Zhang YueSchool of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.ORCID 0000-0001-8434-0325

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the

Indexed as

Ion ChannelsModels, MolecularSupport Vector MachineConotoxinsConotoxinsIon Channels

Identifiers

PMID28497044

DOI10.1155/2017/2929807

PMCPMC5401747

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.