TriNet: A tri-fusion neural network for the prediction of anticancer and antimicrobial peptides

Wanyun Zhou, Yufei Liu, Yingxin Li, Siqi Kong, Weilin Wang, Boyun Ding, Jiyun Han, Chaozhou Mou, Xin Gao, Juntao Liu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)100702
Issue number3
StatePublished - Feb 3 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-03-22
Acknowledgements: This work was supported by the National Key R&D Program of China with code 2020YFA0712400 and the National Natural Science Foundation of China with codes 61801265 and 62272268.


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