Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network - Liu, Zihao; Zhang, Ying; Han, Xudong; Li, Chenxi; Yang, Xuhui; Gao, Jie; Xie, Ganfeng; Du, Nan (Frontiers in Cell and Developmental Biology, 2020)
Millions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. In recent years, Long non-coding RNAs (lncRNA) have been proven to play an important role in diseases, especially cancers. These lncRNAs execute their functions by regulating genes expression. Therefore, identifying lncRNAs which are related to cancers could help researchers gain a deeper understanding of cancer mechanisms and help them find treatment options. A large number of relationships between lncRNAs and cancers has been verified by biological experiments, which give us a chance to use computational methods to identify cancers-related lncRNAs. In this paper, we applied Convolutional Neural Network (CNN) to identify cancers-related lncRNAs by lncRNA’s target genes and their tissue expression specificity. Since lncRNA regulates target genes expression and it has been reported to have tissue expression specificity, their target genes and expression in different tissues were used as features of lncRNAs. Then, Deep Belief Network (DBN) was used to unsupervised encode features of lncRNAs. Finally, CNN was used to predict cancer-related lncRNAs based on known relationship between lncRNAs and cancers. For each type of cancer, we built a CNN model to predict its related lncRNAs. We identified more related lncRNAs for 41 kinds of cancers. 10-cross validation has been used to prove the performance of our method.