ArticleFrontiers in bioengineering and biotechnology2020
Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- RMNet: An RNA m6A Cross-species Methylation Detection Method for Nanopore Sequencing.Current drug targets · 2025Article
- Recent Advances in Peptide Drug Discovery: Novel Strategies and Targeted Protein Degradation.Pharmaceutics · 2024Review
- ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information.International journal of molecular sciences · 2023Article
- Anti-Cancer Peptides: Status and Future Prospects.Molecules (Basel, Switzerland) · 2023Review
- IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach.Computational intelligence and neuroscience · 2023Article
- Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy.Entropy (Basel, Switzerland) · 2022Article
- To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification.Sensors (Basel, Switzerland) · 2022Article
- ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information.Molecules (Basel, Switzerland) · 2022Article
- MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors.Briefings in bioinformatics · 2022Article
- Sequence-Based Prediction of Transmembrane Protein Crystallization Propensity.Interdisciplinary sciences, computational life sciences · 2021Article
- Immunoglobulin Classification Based on FC* and GC* Features.Frontiers in genetics · 2021Article
- ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs.Frontiers in bioengineering and biotechnology · 2021Article
- Identification and Classification of Enhancers Using Dimension Reduction Technique and Recurrent Neural Network.Computational and mathematical methods in medicine · 2020Article
- Integration of Machine Learning and Structural Analysis for Predicting Peptide Antibiofilm Effects: Advancements in Drug Discovery for Biofilm-Related Infections.Iranian journal of pharmaceutical research : IJPRArticle
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Authors and funding
5 authors.
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Abstract
Cancer is still a severe health problem globally. The therapy of cancer traditionally involves the use of radiotherapy or anticancer drugs to kill cancer cells, but these methods are quite expensive and have side effects, which will cause great harm to patients. With the find of anticancer peptides (ACPs), significant progress has been achieved in the therapy of tumors. Therefore, it is invaluable to accurately identify anticancer peptides. Although biochemical experiments can solve this work, this method is expensive and time-consuming. To promote the application of anticancer peptides in cancer therapy, machine learning can be used to recognize anticancer peptides by extracting the feature vectors of anticancer peptides. Nevertheless, poor performance usually be found in training the machine learning model to utilizing high-dimensional features in practice. In order to solve the above job, this paper put forward a 19-dimensional feature model based on anticancer peptide sequences, which has lower dimensionality and better performance than some existing methods. In addition, this paper also separated a model with a low number of dimensions and acceptable performance. The few features identified in this study may represent the important features of anticancer peptides.
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