ArticleOncotarget2016
iACP: a sequence-based tool for identifying anticancer peptides.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 163 papers, 1 of them a synthesis that pooled it.
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Who cites it
163 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Dynamic Fourier wavelet positional encoding for anticancer peptide prediction via transfer learning.iScience · 2026Article
- From Bacteria to Breakthroughs: Design and Evaluation of Gallocin-Based Peptide for Colorectal Cancer Therapeutic.Probiotics and antimicrobial proteins · 2026Article
- SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.Briefings in bioinformatics · 2026Article
- Streptococcus-derived CpsA-CpsC fusion protein induces apoptosis in cervical cancer cells through in silico and in vitro methods.Scientific reports · 2026Article
- Design and classify innovative antimicrobial and dual- and multi-functional peptides using generative artificial intelligence.Scientific reports · 2026Article
- DPAS: disease-associated peptide anomaly score for identifying pathogenic peptides via one-class learning.Scientific reports · 2026Article
- Deep capsule neural network for identifying anticancer peptides using sequence to image transformation-based local embedded features.BMC biology · 2026Article
- Aegis: a transformer-based deep learning framework for the accurate identification of anticancer peptides.BMC biology · 2026Article
- Advances in antimicrobial peptides: promising cancer treatments and vaccines.Frontiers in medicine · 2026Review
- AttBiLSTM_DE: enhancing anticancer peptide prediction using word embedding and an optimized attention-based BiLSTM framework.Scientific reports · 2025Article
- An ensemble strategy for piRNA identification through hybrid moment-based feature modeling.Scientific reports · 2025Article
- iACP-DPNet: a dual-pooling causal dilated convolutional network for interpretable anticancer peptide identification.Functional & integrative genomics · 2025Article
- pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.Scientific reports · 2025Article
- Dynamic Visualization of Computer-Aided Peptide Design for Cancer Therapeutics.Drug design, development and therapy · 2025Review
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- Identification of Multifunctional Putative Bioactive Peptides in the Insect Model Red Palm Weevil (Biomolecules · 2024Article
- PeptiHub: a curated repository of precisely annotated cancer-related peptides with advanced utilities for peptide exploration and discovery.Database : the journal of biological databases and curation · 2024Article
- 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
103 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer remains a major killer worldwide. Traditional methods of cancer treatment are expensive and have some deleterious side effects on normal cells. Fortunately, the discovery of anticancer peptides (ACPs) has paved a new way for cancer treatment. With the explosive growth of peptide sequences generated in the post genomic age, it is highly desired to develop computational methods for rapidly and effectively identifying ACPs, so as to speed up their application in treating cancer. Here we report a sequence-based predictor called iACP developed by the approach of optimizing the g-gap dipeptide components. It was demonstrated by rigorous cross-validations that the new predictor remarkably outperformed the existing predictors for the same purpose in both overall accuracy and stability. For the convenience of most experimental scientists, a publicly accessible web-server for iACP has been established at http://lin.uestc.edu.cn/server/iACP, by which users can easily obtain their desired results.
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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.