计算机科学
鉴定(生物学)
疾病
机器学习
人工智能
特征(语言学)
联想(心理学)
深度学习
自编码
编码器
数据挖掘
模式识别(心理学)
医学
生物
操作系统
认识论
哲学
病理
植物
语言学
作者
Wei Lan,Dehuan Lai,Qingfeng Chen,X. Wu,Baoshan Chen,Jin Liu,Jianxin Wang,Yi‐Ping Phoebe Chen
标识
DOI:10.1109/tcbb.2020.3034910
摘要
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.
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