ArticleBMC bioinformatics2019
Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The importance of non-coding RNAs in environmental stress-related developmental brain disorders: A systematic review of evidence associated with exposure to alcohol, anesthetic drugs, nicotine, and viral infections.Neuroscience and biobehavioral reviews · 2021Pooled it
- The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.Scientific reports · 2025Article
- Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model.Scientific reports · 2024Article
- Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer.International journal of molecular sciences · 2023Review
- Network approaches for modeling the effect of drugs and diseases.Briefings in bioinformatics · 2022Review
- gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network.BMC bioinformatics · 2022Article
- Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network.Frontiers in genetics · 2022Article
- Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.Briefings in bioinformatics · 2021Article
- A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs.BMC bioinformatics · 2021Article
- Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer.Frontiers in genetics · 2021Review
- Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview.Frontiers in genetics · 2021Review
- Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets.Molecular therapy. Nucleic acids · 2020Article
- A random forest based computational model for predicting novel lncRNA-disease associations.BMC bioinformatics · 2020Article
- Identification of the Hub Genes Related to Nerve Injury-Induced Neuropathic Pain.Frontiers in neuroscience · 2020Article
- A novel target convergence set based random walk with restart for prediction of potential LncRNA-disease associations.BMC bioinformatics · 2019Article
- Deep Learning Enables Accurate Prediction of Interplay Between lncRNA and Disease.Frontiers in genetics · 2019Article
- Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms.Frontiers in genetics · 2019Review
- A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder.Frontiers in pharmacology · 2019Article
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5 authors.
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Abstract
backgroundLong non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. However, using experiments to explore the lncRNA-disease associations is expensive and time consuming.
resultsIn this study, we developed a novel method to identify potential lncRNA-disease associations by Integrating Diverse Heterogeneous Information sources with positive pointwise Mutual Information and Random Walk with restart algorithm (namely IDHI-MIRW). IDHI-MIRW first constructs multiple lncRNA similarity networks and disease similarity networks from diverse lncRNA-related and disease-related datasets, then implements the random walk with restart algorithm on these similarity networks for extracting the topological similarities which are fused with positive pointwise mutual information to build a large-scale lncRNA-disease heterogeneous network. Finally, IDHI-MIRW implemented random walk with restart algorithm on the lncRNA-disease heterogeneous network to infer potential lncRNA-disease associations.
conclusionsCompared with other state-of-the-art methods, IDHI-MIRW achieves the best prediction performance. In case studies of breast cancer, stomach cancer, and colorectal cancer, 36/45 (80%) novel lncRNA-disease associations predicted by IDHI-MIRW are supported by recent literatures. Furthermore, we found lncRNA LINC01816 is associated with the survival of colorectal cancer patients. IDHI-MIRW is freely available at https://github.com/NWPU-903PR/IDHI-MIRW .
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