ArticlePatterns (New York, N.Y.)2023
TriNet: A tri-fusion neural network for the prediction of anticancer and antimicrobial peptides.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deep capsule neural network for identifying anticancer peptides using sequence to image transformation-based local embedded features.BMC biology · 2026Article
- Exploring and expanding the chemical multiverse of peptides.Chemical science · 2026Review
- AOPxSVM: A Support Vector Machine for Identifying Antioxidant Peptides Using a Block Substitution Matrix and Amino Acid Composition, Transformation, and Distribution Embeddings.Foods (Basel, Switzerland) · 2025Article
- Dynamic Visualization of Computer-Aided Peptide Design for Cancer Therapeutics.Drug design, development and therapy · 2025Review
- Cationic antimicrobial peptides: potential templates for anticancer agents.Frontiers in medicine · 2025Review
- PepNet: an interpretable neural network for anti-inflammatory and antimicrobial peptides prediction using a pre-trained protein language model.Communications biology · 2024Article
- PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation.Scientific reports · 2024Article
- Article
- An Augmented Sample Selection Framework for Prediction of Anticancer Peptides.Molecules (Basel, Switzerland) · 2023Article
- Using the Random Forest for Identifying Key Physicochemical Properties of Amino Acids to Discriminate Anticancer and Non-Anticancer Peptides.International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate identification of anticancer peptides (ACPs) and antimicrobial peptides (AMPs) remains a computational challenge. We propose a tri-fusion neural network termed TriNet for the accurate prediction of both ACPs and AMPs. The framework first defines three kinds of features to capture the peptide information contained in serial fingerprints, sequence evolutions, and physicochemical properties, which are then fed into three parallel modules: a convolutional neural network module enhanced by channel attention, a bidirectional long short-term memory module, and an encoder module for training and final classification. To achieve a better training effect, TriNet is trained via a training approach using iterative interactions between the samples in the training and validation datasets. TriNet is tested on multiple challenging ACP and AMP datasets and exhibits significant improvements over various state-of-the-art methods. The web server and source code of TriNet are respectively available at http://liulab.top/TriNet/server and https://github.com/wanyunzh/TriNet.
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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.