DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information.

Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information.

A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.

Motivation: A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data.

LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model.

LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA-protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed.

MFIDMA: A Multiple Information Integration Model for the Prediction of Drug-miRNA Associations.

Abnormal microRNA (miRNA) functions play significant roles in various pathological processes. Thus, predicting drug-miRNA associations (DMA) may hold great promise for identifying the potential targets of drugs. However, discovering the associations between drugs and miRNAs through wet experiments is time-consuming and laborious.

PPAEDTI: Personalized Propagation Auto-Encoder Model for Predicting Drug-Target Interactions.

Identifying protein targets for drugs establishes an indispensable knowledge foundation for drug repurposing and drug development. Though expensive and time-consuming, vitro trials are widely employed to discover drug targets, and the existing relevant computational algorithms still cannot satisfy the demand for real application in drug R&D with regards to the prediction accuracy and performance efficiency, which are urgently needed to be improved. To this end, we propose here the PPAEDTI model, which uses the graph personalized propagation technique to predict drug-target interactions from the known interaction network.

SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes.

Computational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computational model combining multimodal information and graph convolutional neural networks, combines node similarity to form node information and then predicts associated nodes using GCN with a distributive contribution mechanism. The model can be used not only to predict the molecular level of circRNA-miRNA interactions but also to predict circRNA-cancer and circRNA-gene associations.

KGDCMI: A New Approach for Predicting circRNA-miRNA Interactions From Multi-Source Information Extraction and Deep Learning.

Emerging evidence has revealed that circular RNA (circRNA) is widely distributed in mammalian cells and functions as microRNA (miRNA) sponges involved in transcriptional and posttranscriptional regulation of gene expression. Recognizing the circRNA-miRNA interaction provides a new perspective for the detection and treatment of human complex diseases. Compared with the traditional biological experimental methods used to predict the association of molecules, which are limited to the small-scale and are time-consuming and laborious, computing models can provide a basis for biological experiments at low cost.

BNEMDI: A Novel MicroRNA-Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network.

As a novel target in pharmacy, microRNA (miRNA) can regulate gene expression under specific disease conditions to produce specific proteins. To date, many researchers leveraged miRNA to reveal drug efficacy and pathogenesis at the molecular level. As we all know that conventional wet experiments suffer from many problems, including time-consuming, labor-intensity, and high cost.

SAWRPI: A Stacking Ensemble Framework With Adaptive Weight for Predicting ncRNA-Protein Interactions Using Sequence Information.

Non-coding RNAs (ncRNAs) take essential effects on biological processes, like gene regulation. One critical way of ncRNA executing biological functions is interactions between ncRNA and RNA binding proteins (RBPs). Identifying proteins, involving ncRNA-protein interactions, can well understand the function ncRNA.