Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/178058
Title: Knowledge-based dual external attention network for peptide detectability prediction
Authors: Zhang, Xiaocai
Peng, Hui
Tang, Tao
Liu, Yuansheng
Wang, Yang
Zhang, Jianjia
Keywords: Medicine, Health and Life Sciences
Issue Date: 2024
Source: Zhang, X., Peng, H., Tang, T., Liu, Y., Wang, Y. & Zhang, J. (2024). Knowledge-based dual external attention network for peptide detectability prediction. Knowledge-Based Systems, 286, 111378-. https://dx.doi.org/10.1016/j.knosys.2024.111378
Journal: Knowledge-Based Systems
Abstract: Accurate prediction of peptide detectability plays a crucial role in various proteomics data analyses such as peptide identification and protein quantification. To improve the precision of peptide detectability prediction, we propose a novel approach called the Knowledge-based Dual External Attention Network (KDEAN). KDEAN introduces several innovative elements to enhance its representation and prediction capabilities. Firstly, it extracts valuable knowledge-based features from peptide sequences to facilitate the pattern recognition process. Secondly, KDEAN adopted dual networks to separately train peptide sequences from both the forward and backward directions, capturing comprehensive information. Thirdly, an external attention mechanism is utilized to identify and understand the connections between different peptide samples. The structure of KDEAN enables long-term dependency learning from both directions of the peptide sequences. Extensive evaluations on four testing datasets demonstrate that KDEAN outperforms existing methods, achieving a higher average performance in peptide detectability prediction. Additionally, comprehensive ablation studies confirm the effectiveness and advantages of key components in KDEAN, including knowledge-based feature representation, dual network architecture, and external attention.
URI: https://hdl.handle.net/10356/178058
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2024.111378
Schools: School of Biological Sciences 
Rights: © 2024 Elsevier B.V. All rights reserved
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SBS Journal Articles

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