CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA–Disease Associations

Abstract

It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA–disease associations are helpful for understanding the molecular pathogenesis of diseases. Most previous methods have concentrated on the construction of shallow learning models in order to predict lncRNA-disease associations, while they have failed to deeply integrate heterogeneous multi-source data and to learn the low-dimensional feature representations from these data. We propose a method based on the convolutional neural network with the attention mechanism and convolutional autoencoder for predicting candidate disease-related lncRNAs, and refer to it as CNNDLP. CNNDLP integrates multiple kinds of data from heterogeneous sources, including the associations, interactions, and similarities related to the lncRNAs, diseases, and miRNAs. Two different embedding layers are established by combining the diverse biological premises about the cases that the lncRNAs are likely to associate with the diseases. We construct a novel prediction model based on the convolutional neural network with attention mechanism and convolutional autoencoder to learn the attention and the low-dimensional network representations of the lncRNA–disease pairs from the embedding layers. The different adjacent edges among the lncRNA, miRNA, and disease nodes have different contributions for association prediction. Hence, an attention mechanism at the adjacent edge level is established, and the left side of the model learns the attention representation of a pair of lncRNA and disease. A new type of lncRNA similarity and a new type of disease similarity are calculated by incorporating the topological structures of multiple bipartite networks. The low-dimensional network representation of the lncRNA-disease pairs is further learned by the autoencoder based convolutional neutral network on the right side of the model. The cross-validation experimental results confirm that CNNDLP has superior prediction performance compared to the state-of-the-art methods. Case studies on stomach cancer, breast cancer, and prostate cancer further show the ability of CNNDLP for discovering the potential disease lncRNAs.

A random forest based computational model for predicting novel lncRNA-disease associations

“…Guo et al implemented a random forest classifier based model for inferring novel associations among various bimolecular by constructing a molecular association network based on the known associations among diseases, proteins, miRNAs, lncRNAs and drugs [43]. Latterly, Xuan et al proposed a series of convolutional neural network based LDA prediction models, including CNNLDA [44], GCNLDA [45], CNNDLP [46] and LDAPred [47]. CNNLDA learned the global and attention characteristics of lncRNA-disease pairs using convolutional neural networks by integrating the DSS, the LFS, and the lncRNA-disease, miRNAdisease and lncRNA-miRNA associations [44].…”

“…GCNLDA learned the local and network characteristics of lncRNA-disease pairs using convolutional neural network, graph convolutional network and convolutional auto-encoder by combining multiple associations among diseases, miRNAs and lncRNAs [45]. CNNDLP learned the network and attention characteristics of lncRNA-disease pairs using convolutional neural network and convolutional auto-encoder by integrating various associations, interactions and similarities among miRNAs, lncRNAs and diseases [46]. LDAPred implemented LDA prediction using convolutional neural network and information flow propagation by integrating various associations, interactions, similarities and topology among miRNAs, lncRNAs and disease [47].…”

LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

“…In recent years, machine learning has been used to infer lncRNA-disease associations [23][24][25][26][27][28][29]. Zhao et al [30] integrated multi-omic data, genomic, regulome, and transcriptome with naïve Bayesian classifier models to predict lncRNA-cancer associations, and successfully identified 707 potential cancer-related lncRNAs.…”

A Novel lncRNA-Disease Association Prediction Model Using Laplacian Regularized Least Squares and Space Projection-Federated Method

“…Many researchers attempted to use deep learning in the field of lncRNA association prediction. Xuan et al proposed a variety of methods on the basis of convolutional neural network for the prediction of disease-related lncRNAs [45][46][47]. In recent years, other machine learning-based models, such as randomized matrix completion algorithm [48], inductive matrix completion algorithm [49], and matrix factorization algorithm [50], have been put forward to predict lncRNA-associated diseases.Liu et al [51]proposed a weighted graph regulated collaborative matrix factorization method to identify lncRNA-disease associations; Xuan et al [52] used the probabilistic matrix factorization method to predict disease-related lncRNAs; Zeng et al [53] integrated alternative lead squares and matrix factorization to identify lncRNA-disease associations; Zeng et al [54]integrated deep learning and matrix factorization to identify lncRNA-disease association.However, parameters are difficult to determine via such methods.Xuan et al [52] used the probabilistic matrix factorization method to predict disease-related lncRNAs.…”