ArticleMolecular therapy. Nucleic acids2021
Prediction of lncRNA-disease associations via an embedding learning HOPE in heterogeneous information networks.
Article in Molecular therapy. Nucleic acids, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph.Journal of pharmaceutical analysis · 2025Article
- Review
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- A Novel Framework for Data Assessment That Uses Edge Technology to Improve the Detection of Communicable Diseases.Diagnostics (Basel, Switzerland) · 2024Article
- DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge.Briefings in bioinformatics · 2024Article
- Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer.International journal of molecular sciences · 2023Review
- Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Hierarchical graph attention network for miRNA-disease association prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
- SCCPMD: Probability matrix decomposition method subject to corrected similarity constraints for inferring long non-coding RNA-disease associations.Frontiers in microbiology · 2022Article
- Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations.Frontiers in genetics · 2022Article
- Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview.Frontiers in genetics · 2021Review
- GBDTLRL2D Predicts LncRNA-Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network.Frontiers in cell and developmental biology · 2021Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Uncovering additional long non-coding RNA (lncRNA)-disease associations has become increasingly important for developing treatments for complex human diseases. Identification of lncRNA biomarkers and lncRNA-disease associations is central to diagnoses and treatment. However, traditional experimental methods are expensive and time-consuming. Enormous amounts of data present in public biological databases are available for computational methods used to predict lncRNA-disease associations. In this study, we propose a novel computational method to predict lncRNA-disease associations. More specifically, a heterogeneous network is first constructed by integrating the associations among microRNA (miRNA), lncRNA, protein, drug, and disease, Second, high-order proximity preserved embedding (HOPE) was used to embed nodes into a network. Finally, the rotation forest classifier was adopted to train the prediction model. In the 5-fold cross-validation experiment, the area under the curve (AUC) of our method achieved 0.8328 ± 0.0236. We compare it with the other four classifiers, in which the proposed method remarkably outperformed other comparison methods. Otherwise, we constructed three case studies for three excess death rate cancers, respectively. The results show that 9 (lung cancer, gastric cancer, and hepatocellular carcinomas) out of the top 15 predicted disease-related lncRNAs were confirmed by our method. In conclusion, our method could predict the unknown lncRNA-disease associations effectively.
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