ArticleScientific reports2016
Identifying anticancer peptides by using improved hybrid compositions.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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The trial behind it
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Who cites it
36 citing papers in PubMed.
- SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.Briefings in bioinformatics · 2026Article
- Fish By-Products Utilization in Food and Health: Extraction Technologies, Bioactive, and Sustainability Challenges.Food science & nutrition · 2025Review
- EnDM-CPP: A Multi-view Explainable Framework Based on Deep Learning and Machine Learning for Identifying Cell-Penetrating Peptides with Transformers and Analyzing Sequence Information.Interdisciplinary sciences, computational life sciences · 2025Article
- iACP-DPNet: a dual-pooling causal dilated convolutional network for interpretable anticancer peptide identification.Functional & integrative genomics · 2025Article
- iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.Current medicinal chemistry · 2025Article
- Extended dipeptide composition framework for accurate identification of anticancer peptides.Scientific reports · 2024Article
- PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation.Scientific reports · 2024Article
- Integrating In Silico and In Vitro Approaches to Identify Natural Peptides with Selective Cytotoxicity against Cancer Cells.International journal of molecular sciences · 2024Article
- A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features.Interdisciplinary sciences, computational life sciences · 2024Article
- Contrastive learning for enhancing feature extraction in anticancer peptides.Briefings in bioinformatics · 2024Article
- An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM.PeerJ. Computer science · 2024Article
- A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides.Molecules (Basel, Switzerland) · 2023Article
- AI-DrugNet: A network-based deep learning model for drug repurposing and combination therapy in neurological disorders.Computational and structural biotechnology journal · 2023Article
- ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides.International journal of molecular sciences · 2022Article
- Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides.ChemMedChem · 2022Article
- To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification.Sensors (Basel, Switzerland) · 2022Article
- BBPpredict: A Web Service for Identifying Blood-Brain Barrier Penetrating Peptides.Frontiers in genetics · 2022Article
- LMPred: predicting antimicrobial peptides using pre-trained language models and deep learning.Bioinformatics advances · 2022Article
- Conjugation with gold nanoparticles improves the stability of the KT2 peptide and maintains its anticancer properties.RSC advances · 2021Article
- Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.Briefings in bioinformatics · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is one of the main causes of threats to human life. Identification of anticancer peptides is important for developing effective anticancer drugs. In this paper, we developed an improved predictor to identify the anticancer peptides. The amino acid composition (AAC), the average chemical shifts (acACS) and the reduced amino acid composition (RAAC) were selected to predict the anticancer peptides by using the support vector machine (SVM). The overall prediction accuracy reaches to 93.61% in jackknife test. The results indicated that the combined parameter was helpful to the prediction for anticancer peptides.
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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.