ArticleFrontiers in genetics2021
ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 65 citations in OpenAlex.
- GDA-Pred: Generative AI-Driven Data Augmentation for Improved Prediction of IL-6 and IL-13-Inducing Peptides.International journal of molecular sciences · 2026Article
- ACP-EPC: an interpretable deep learning framework for anticancer peptide prediction utilizing pre-trained protein language model and multi-view feature extracting strategy.Molecular diversity · 2026Article
- Deep capsule neural network for identifying anticancer peptides using sequence to image transformation-based local embedded features.BMC biology · 2026Article
- Artificial intelligence-driven anticancer peptide discovery.iMetaOmics · 2025Review
- Multi-modal contrastive drug synergy prediction model guided by single modality.Journal of cheminformatics · 2025Article
- BPFun: a deep learning framework for bioactive peptide function prediction using multi-label strategy by transformer-driven and sequence rich intrinsic information.BMC bioinformatics · 2025Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning.International journal of molecular sciences · 2024Article
- LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets.Molecular diversity · 2024Article
- A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features.Interdisciplinary sciences, computational life sciences · 2024Article
- Efficient prediction of anticancer peptides through deep learning.PeerJ. Computer science · 2024Article
- Prediction of anticancer peptides derived from the true lectins ofFrontiers in pharmacology · 2024Article
- ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization.Frontiers in genetics · 2024Article
- 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
- An Augmented Sample Selection Framework for Prediction of Anticancer Peptides.Molecules (Basel, Switzerland) · 2023Article
- Efficient Mining of Anticancer Peptides from Gut Metagenome.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Anti-Cancer Peptides: Status and Future Prospects.Molecules (Basel, Switzerland) · 2023Review
- Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning.Antibiotics (Basel, Switzerland) · 2022Review
- ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides.International journal of molecular sciences · 2022Article
- Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics.Pharmaceutics · 2022Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anticancer peptides (ACPs) have provided a promising perspective for cancer treatment, and the prediction of ACPs is very important for the discovery of new cancer treatment drugs. It is time consuming and expensive to use experimental methods to identify ACPs, so computational methods for ACP identification are urgently needed. There have been many effective computational methods, especially machine learning-based methods, proposed for such predictions. Most of the current machine learning methods try to find suitable features or design effective feature learning techniques to accurately represent ACPs. However, the performance of these methods can be further improved for cases with insufficient numbers of samples. In this article, we propose an ACP prediction model called ACP-DA (Data Augmentation), which uses data augmentation for insufficient samples to improve the prediction performance. In our method, to better exploit the information of peptide sequences, peptide sequences are represented by integrating binary profile features and AAindex features, and then the samples in the training set are augmented in the feature space. After data augmentation, the samples are used to train the machine learning model, which is used to predict ACPs. The performance of ACP-DA exceeds that of existing methods, and ACP-DA achieves better performance in the prediction of ACPs compared with a method without data augmentation. The proposed method is available at http://github.com/chenxgscuec/ACPDA.
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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.