ReviewMolecules (Basel, Switzerland)2017
Recent Advances in Conotoxin Classification by Using Machine Learning Methods.
Review in Molecules (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 64 citations in OpenAlex.
- Molecular arms race classifier for decrypting venom peptide and ion channel interactions.Digital discovery · 2026Article
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- In Silico Conotoxin Studies: Progress and Prospects.Molecules (Basel, Switzerland) · 2024Review
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- Voltage-Gated KMarine drugs · 2024Review
- DeTox: a pipeline for the detection of toxins in venomous organisms.Briefings in bioinformatics · 2024Article
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- ASmiR: a machine learning framework for prediction of abiotic stress-specific miRNAs in plants.Functional & integrative genomics · 2023Article
- Immunosensors for Assay of Toxic Biological Warfare Agents.Biosensors · 2023Review
- Synthesis and evaluation of a novel analgesic conotoxin Lt7b that inhibits calcium currents and increases sodium currents.Journal of cellular and molecular medicine · 2022Article
- ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with PseudoInternational journal of molecular sciences · 2022Article
- Article
- Comprehensive analysis reveals a 5-gene signature and immune cell infiltration in Alzheimer's disease with qPCR validation.Frontiers in genetics · 2022Article
- High-Throughput Prediction and Design of Novel Conopeptides for Biomedical Research and Development.Biodesign research · 2022Article
- Mining the Royal Jelly Proteins: Combinatorial Hexapeptide Ligand Library Significantly Improves the MS-Based Proteomic Identification in Complex Biological Samples.Molecules (Basel, Switzerland) · 2021Article
- Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis.Molecular therapy. Nucleic acids · 2020Article
- Classification of drug molecules for oxidative stress signalling pathway.IET systems biology · 2019Article
- Structural and Functional Analyses of Cone Snail Toxins.Marine drugs · 2019Review
- Snails In Silico: A Review of Computational Studies on the Conopeptides.Marine drugs · 2019Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.
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Registered trials
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.