ArticleScientific reports2017
Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou's pseudo components.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 40 citations in OpenAlex.
- BiToxNet: a deep learning framework integrating multimodal features for accurate identification of neurotoxic peptides and proteins.BMC biology · 2026Article
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- A large language model for predicting neurotoxic peptides and neurotoxins.Protein science : a publication of the Protein Society · 2025Article
- Function Prediction of Peptide Toxins with Sequence-Based Multi-Tasking PU Learning Method.Toxins · 2022Article
- Some illuminating remarks on molecular genetics and genomics as well as drug development.Molecular genetics and genomics : MGG · 2020Review
- High temperatures affect the hypersensitive reaction, disease resistance and gene expression induced by a novel harpin HpaG-Xcm.Scientific reports · 2019Article
- Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia.Molecular therapy. Nucleic acids · 2018Article
- Tick Paralysis: Solving an Enigma.Veterinary sciences · 2018Review
- Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Presynaptic and postsynaptic neurotoxins are two groups of neurotoxins. Identification of presynaptic and postsynaptic neurotoxins is an important work for numerous newly found toxins. It is both costly and time consuming to determine these two neurotoxins by experimental methods. As a complement, using computational methods for predicting presynaptic and postsynaptic neurotoxins could provide some useful information in a timely manner. In this study, we described four algorithms for predicting presynaptic and postsynaptic neurotoxins from sequence driven features by using Increment of Diversity (ID), Multinomial Naive Bayes Classifier (MNBC), Random Forest (RF), and K-nearest Neighbours Classifier (IBK). Each protein sequence was encoded by pseudo amino acid (PseAA) compositions and three biological motif features, including MEME, Prosite and InterPro motif features. The Maximum Relevance Minimum Redundancy (MRMR) feature selection method was used to rank the PseAA compositions and the 50 top ranked features were selected to improve the prediction accuracy. The PseAA compositions and three kinds of biological motif features were combined and 12 different parameters that defined as P1-P12 were selected as the input parameters of ID, MNBC, RF, and IBK. The prediction results obtained in this study were significantly better than those of previously developed methods.
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