LDICDL: LncRNA-Disease Association Identification Based on Collaborative Deep Learning
| Autor: | Qingfeng Chen, Jianxin Wang, Jin Liu, Ximin Wu, Dehuan Lai, Baoshan Chen, Wei Lan, Yi-Ping Phoebe Chen |
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| Rok vydání: | 2022 |
| Předmět: |
Computer science
0206 medical engineering 02 engineering and technology Disease Machine learning computer.software_genre Cross-validation Matrix decomposition Deep Learning Genetics Humans Biological data business.industry Applied Mathematics Deep learning Computational Biology Identification (information) Feature (computer vision) RNA Long Noncoding Artificial intelligence business Encoder computer Algorithms 020602 bioinformatics Biotechnology |
| Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19:1715-1723 |
| ISSN: | 2374-0043 1545-5963 |
| Popis: | It has been proved that long noncoding RNA (lncRNA) plays critical roles in many human diseases. Therefore, inferring associations between lncRNAs and diseases can contribute to disease diagnosis, prognosis and treatment. To overcome the limitation of traditional experimental methods such as expensive and time-consuming, several computational methods have been proposed to predict lncRNA-disease associations by fusing different biological data. However, the prediction performance of lncRNA-disease associations identification needs to be improved. In this study, we propose a computational model (named LDICDL) to identify lncRNA-disease associations based on collaborative deep learning. It uses an automatic encoder to denoise multiple lncRNA feature information and multiple disease feature information, respectively. Then, the matrix decomposition algorithm is employed to predict the potential lncRNA-disease associations. In addition, to overcome the limitation of matrix decomposition, the hybrid model is developed to predict associations between new lncRNA (or disease) and diseases (or lncRNA). The ten-fold cross validation and de novo test are applied to evaluate the performance of method. The experimental results show LDICDL outperforms than other state-of-the-art methods in prediction performance. |
| Databáze: | OpenAIRE |
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