ArticleBriefings in bioinformatics2023
iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it, 90 citations in OpenAlex.
- 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
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Nano-antimicrobial peptides (Nano-AMPs) to combat resistant gram-negative bacteria.Drug delivery and translational research · 2026Review
- Towards Safer Antimicrobial Peptide Therapeutics: A Predictive-Generative Framework Targeting ESKAPE Pathogens.Probiotics and antimicrobial proteins · 2026Article
- A unified framework for potency-oriented AMP discovery via multi-modal learning and guided sequence synthesis.PLoS computational biology · 2026Article
- SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.Briefings in bioinformatics · 2026Article
- Engineering host‑defense peptides enhanced by artificial intelligence and nano delivery systems to overcome biofilms and antimicrobial resistance.Engineering microbiology · 2026Review
- MAPLE: interpretable deep learning identifies selective antimicrobial peptides using joint evolutionary-physicochemical analysis.Briefings in bioinformatics · 2026Article
- CLABP: a contrastive learning framework integrating protein language models and structural information for antibacterial peptide prediction.Briefings in bioinformatics · 2026Article
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Biochemical-knowledge-driven machine learning pipeline for generating potent antimicrobial peptides.Briefings in bioinformatics · 2026Article
- tsAMP: a strain-level antimicrobial peptide identification framework based on large language models and pathogen genomic variation.Frontiers in microbiology · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Article
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- Hybrid and conjugated antimicrobial peptides: new tactics to counter bacterial resistance.Frontiers in microbiology · 2026Review
- Machine learning-driven discovery of antimicrobial peptides againstFrontiers in pharmacology · 2026Article
- Aquatic-derived antimicrobial peptides and their strategically modified analogues as prospective anticancer therapeutics: a comprehensive systematic review of enhancement methodologies and mechanistic insights.Frontiers in chemistry · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- AttBiLSTM_DE: enhancing anticancer peptide prediction using word embedding and an optimized attention-based BiLSTM framework.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 9 institutions in 5 countries.
Funding
Abstract
Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.
Indexed as
Identifiers
What OpenQuestion holds
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.