Oct 5, 2020 · In this paper, we discuss how this problem is formulated as a link prediction task in a bipartite graph and construct a computational model to ...
An efficient approach based on multi-sources information to predict circRNA-disease associations using deep convolutional neural network · Computer Science, ...
May 30, 2023 · To apply CNNs to miRNA sequence data, First, miRNA sequences are defined as “words” consisting of n -length bases (Dong et al., 2006; Costa and ...
Dec 26, 2022 · In this paper, a multiple features integration model (MFIDMA) is proposed to predict drug–miRNA association. Specifically, we first formulated ...
In this work, we propose a novel computational method, named BNEMDI, which predicts miRNA–drug interactions using drug substructure fingerprint, miRNA sequence, ...
Based on the idea of ensemble learning, we integrate graph neural networks (GNNs) and convolutional neural networks (CNNs) to propose a miRNA and small molecule ...
The identification of the relationships between SMs and miRNAs is expected to support drug repurposing research on miRNA-related diseases [28]. Different SM- ...
Jun 26, 2018 · Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that ...
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Dec 19, 2022 · In this paper, a multiple features integration model (MFIDMA) is proposed to predict drug–miRNA association. Specifically, we first ...
Jul 3, 2022 · human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem ...