AUTHOR=Liu Zihao , Zhang Ying , Han Xudong , Li Chenxi , Yang Xuhui , Gao Jie , Xie Ganfeng , Du Nan 

TITLE=Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 8 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2020.00637

DOI=10.3389/fcell.2020.00637

ISSN=2296-634X

ABSTRACT=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.