PLMTHP: An Ensemble Framework for Tumor Homing Peptide Prediction based on Protein Language Model
文献类型： 会议论文
第一作者： Yongbing Chen
作者： Yongbing Chen 1 ; Han Qin 1 ; Xiaodan Cui 1 ; Tianhui Yang 1 ; Pingping Sun 1 ; Jianting Gong 2 ; Zilin Ren 2 ;
作者机构： 1.School of Information Science and Technology, Northeast Normal University, Changchun, China
2.Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, China
关键词： Proteins;Support vector machines;Protein engineering;Peptides;Training data;Predictive models;Feature extraction
会议名称： IEEE International Conference on Bioinformatics and Biomedicine
主办单位：
页码： 841-846
摘要： Tumor homing peptides (THPs) play significant role in recognizing and specifically binding to tumor cells. Traditional experimental methods can accurately identify THPs but often suffer from high measurement costs and long experimental cycles. In-silico methods can rapidly screen THPs and accelerate the THPs identification process. Existing THPs prediction methods primarily rely on constructing peptide sequence features and machine learning approaches. These tools rely on manual feature extraction, and the combination of features is crucial for both model performance and robustness. In this study, we propose a method called PLMTHP based on protein language model and integrate multiple machine learning models. Compared to existing models, PLMTHP improves predictions by incorporating high-dimensional features encoded through a protein language model. Our method achieved impressive performance on an independent test set, with ACC of 0.915, MCC of 0.831, and AUC of 0.964. PLMTHP can be downloaded from the following website: https://github.com/Chenyb939/PLMTHP
分类号： q81-53
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