计算机科学
机器学习
可靠性
人工智能
人工神经网络
图形
限制
编码(内存)
特征(语言学)
深度学习
数据挖掘
理论计算机科学
政治学
法学
机械工程
语言学
哲学
工程类
作者
Chang Liu,Cuinan Yu,Yipin Lei,Kangbo Lyu,Tingzhong Tian,Qianhao Li,Dan Zhao,Fengfeng Zhou,Jianyang Zeng
标识
DOI:10.1142/9789811270611_0015
摘要
Identifying effective target-disease associations (TDAs) can alleviate the tremendous cost incurred by clinical failures of drug development. Although many machine learning models have been proposed to predict potential novel TDAs rapidly, their credibility is not guaranteed, thus requiring extensive experimental validation. In addition, it is generally challenging for current models to predict meaningful associations for entities with less information, hence limiting the application potential of these models in guiding future research. Based on recent advances in utilizing graph neural networks to extract features from heterogeneous biological data, we develop CreaTDA, an end-to-end deep learning-based framework that effectively learns latent feature representations of targets and diseases to facilitate TDA prediction. We also propose a novel way of encoding credibility information obtained from literature to enhance the performance of TDA prediction and predict more novel TDAs with real evidence support from previous studies. Compared with state-of-the-art baseline methods, CreaTDA achieves substantially better prediction performance on the whole TDA network and its sparse sub-networks containing the proteins associated with few known diseases. Our results demonstrate that CreaTDA can provide a powerful and helpful tool for identifying novel target-disease associations, thereby facilitating drug discovery.
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