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MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model.
Liang, Ying; Zhang, Ze-Qun; Liu, Nian-Nian; Wu, Ya-Nan; Gu, Chang-Long; Wang, Ying-Long.
Afiliação
  • Liang Y; College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China.
  • Zhang ZQ; College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China.
  • Liu NN; College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China.
  • Wu YN; College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China.
  • Gu CL; College of Information Science and Engineering, Hunan University, Changsha, China.
  • Wang YL; College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China. wangyl@jxau.edu.cn.
BMC Bioinformatics ; 23(1): 189, 2022 May 19.
Article em En | MEDLINE | ID: mdl-35590258
BACKGROUND: Many long non-coding RNAs (lncRNAs) have key roles in different human biologic processes and are closely linked to numerous human diseases, according to cumulative evidence. Predicting potential lncRNA-disease associations can help to detect disease biomarkers and perform disease analysis and prevention. Establishing effective computational methods for lncRNA-disease association prediction is critical. RESULTS: In this paper, we propose a novel model named MAGCNSE to predict underlying lncRNA-disease associations. We first obtain multiple feature matrices from the multi-view similarity graphs of lncRNAs and diseases utilizing graph convolutional network. Then, the weights are adaptively assigned to different feature matrices of lncRNAs and diseases using the attention mechanism. Next, the final representations of lncRNAs and diseases is acquired by further extracting features from the multi-channel feature matrices of lncRNAs and diseases using convolutional neural network. Finally, we employ a stacking ensemble classifier, consisting of multiple traditional machine learning classifiers, to make the final prediction. The results of ablation studies in both representation learning methods and classification methods demonstrate the validity of each module. Furthermore, we compare the overall performance of MAGCNSE with that of six other state-of-the-art models, the results show that it outperforms the other methods. Moreover, we verify the effectiveness of using multi-view data of lncRNAs and diseases. Case studies further reveal the outstanding ability of MAGCNSE in the identification of potential lncRNA-disease associations. CONCLUSIONS: The experimental results indicate that MAGCNSE is a useful approach for predicting potential lncRNA-disease associations.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: RNA Longo não Codificante Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: RNA Longo não Codificante Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China País de publicação: Reino Unido