青岛科技大学  English 
王明辉
赞  

教师拼音名称:wangminghui

手机版

访问量:

最后更新时间:..

ECA-PHV: Predicting human-virus protein-protein interactions through an interpretable model of effective channel attention mechanism

关键字:AMINO-ACID-COMPOSITION; COVARIANCE; REGRESSION; SELECTION

摘要:The prediction of human-virus protein-protein interactions (human-virus PPIs) is significant for exploring the mechanisms of viral infection, making their prediction a necessary and practically valuable research topic. Since conventional methods for the determination of human-virus protein-protein interactions are very complex and expensive, the construction of models plays a crucial role. In this paper, we construct an interpretable model, ECA-PHV, to predict human-virus protein-protein interactions based on an effective channel attention mechanism. First, we utilize five coding modalities, namely AAC, DDE, MMI, CT, and GTPC, to extract the hidden biological information in protein sequences. Individual feature weights are then learned by using a differential evolutionary algorithm that employs weighted combinations to adequately represent various protein sequence information. Next, irrelevant features in multi-information fusion are removed by Group Lasso. Finally, the prediction model is constructed by combining effective channel attention, BiGRU, and 1D-CNN. Compared with existing models, the interpretability framework ECA-PHV proposed in this paper has competitive and stable predictive performance. This shows that our model can efficiently focus on important information about protein sequences. In conclusion, this study accelerates the exploration of human-virus protein-protein interactions and provides some insights of practical value for probing human-virus relationships.

卷号:247

期号:

是否译文:

崂山校区 - 山东省青岛市松岭路99号   
四方校区 - 山东省青岛市郑州路53号   
中德国际合作区(中德校区) - 山东省青岛市西海岸新区团结路3698号
高密校区 - 山东省高密市杏坛西街1号   
济南校区 - 山东省济南市文化东路80号©2015 青岛科技大学    
管理员邮箱:master@qust.edu.cn