ArticleInternational journal of molecular sciences2018
Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- DGNMDA: Dual Heterogeneous Graph Neural Network Encoder for miRNA-Disease Association Prediction.Bioengineering (Basel, Switzerland) · 2024Article
- Predicting miRNA-Disease Associations by Combining Graph and Hypergraph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2024Article
- Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review.AIMS public health · 2024Review
- Special Protein or RNA Molecules Computational Identification.International journal of molecular sciences · 2023Article
- Review
- Review
- Machine learning in the development of targeting microRNAs in human disease.Frontiers in genetics · 2022Review
- Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification.Frontiers in oncology · 2022Review
- GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction.PLoS computational biology · 2021Article
- Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era.Current oncology (Toronto, Ont.) · 2021Review
- The miR-93-3p/ZFP36L1/ZFX axis regulates keratinocyte proliferation and migration during skin wound healing.Molecular therapy. Nucleic acids · 2021Article
- A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.PloS one · 2021Article
- Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks.Frontiers in genetics · 2021Article
- Application of artificial intelligence to the diagnosis and therapy of colorectal cancer.American journal of cancer research · 2020Review
- MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection.Frontiers in genetics · 2020Article
- Inferring Disease-Associated MicroRNAs Using Semi-supervised Multi-Label Graph Convolutional Networks.iScience · 2019Article
- LSGSP: a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection federated method.RSC advances · 2019Article
- Article
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Authors and funding
5 authors.
Funding
Abstract
Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNAs and diseases. However, these methods fail to learn the deep features of the miRNA similarities, the disease similarities, and the miRNA⁻disease associations. We propose a dual convolutional neural network-based method for predicting candidate disease miRNAs and refer to it as CNNDMP. CNNDMP not only exploits the similarities and associations of miRNAs and diseases, but also captures the topology structures of the miRNA and disease networks. An embedding layer is constructed by combining the biological premises about the miRNA⁻disease associations. A new framework based on the dual convolutional neural network is presented for extracting the deep feature representation of associations. The left part of the framework focuses on integrating the original similarities and associations of miRNAs and diseases. The novel miRNA and disease similarities which contain the topology structures are obtained by random walks on the miRNA and disease networks, and their deep features are learned by the right part of the framework. CNNDMP achieves the superior prediction performance than several state-of-the-art methods during the cross-validation process. Case studies on breast cancer, colorectal cancer and lung cancer further demonstrate CNNDMP's powerful ability of discovering potential disease miRNAs.
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