ArticleCells2019
Construction and Comprehensive Analysis of a Molecular Association Network via lncRNA-miRNA -Disease-Drug-Protein Graph.
Article in Cells, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- A Multi-Modal Graph Neural Network Framework for Parkinson's Disease Therapeutic Discovery.International journal of molecular sciences · 2025Article
- A heterogeneous information network learning model with neighborhood-level structural representation for predicting lncRNA-miRNA interactions.Computational and structural biotechnology journal · 2024Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care.Frontiers in genetics · 2023Article
- Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network.Frontiers in genetics · 2022Article
- DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association.Molecular therapy : the journal of the American Society of Gene Therapy · 2021Article
- MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm.Briefings in bioinformatics · 2021Article
- A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.PloS one · 2021Article
- MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning.Frontiers in genetics · 2021Article
- MIPDH: A Novel Computational Model for Predicting microRNA-mRNA Interactions by DeepWalk on a Heterogeneous Network.ACS omega · 2020Article
- Bioentity2vec: Attribute- and behavior-driven representation for predicting multi-type relationships between bioentities.GigaScience · 2020Article
- A random forest based computational model for predicting novel lncRNA-disease associations.BMC bioinformatics · 2020Article
- Integrative Construction and Analysis of Molecular Association Network in Human Cells by Fusing Node Attribute and Behavior Information.Molecular therapy. Nucleic acids · 2020Article
- iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm.Computational and structural biotechnology journal · 2020Article
- Prediction of Drug-Target Interactions From Multi-Molecular Network Based on Deep Walk Embedding Model.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One key issue in the post-genomic era is how to systematically describe the associations between small molecule transcripts or translations inside cells. With the rapid development of high-throughput "omics" technologies, the achieved ability to detect and characterize molecules with other molecule targets opens the possibility of investigating the relationships between different molecules from a global perspective. In this article, a molecular association network (MAN) is constructed and comprehensively analyzed by integrating the associations among miRNA, lncRNA, protein, drug, and disease, in which any kind of potential associations can be predicted. More specifically, each node in MAN can be represented as a vector by combining two kinds of information including the attribute of the node itself (e.g., sequences of ncRNAs and proteins, semantics of diseases and molecular fingerprints of drugs) and the behavior of the node in the complex network (associations with other nodes). A random forest classifier is trained to classify and predict new interactions or associations between biomolecules. In the experiment, the proposed method achieved a superb performance with an area under curve (AUC) of 0.9735 under a five-fold cross-validation, which showed that the proposed method could provide new insight for exploration of the molecular mechanisms of disease and valuable clues for disease treatment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.