ArticleComputational and structural biotechnology journal2024
A heterogeneous information network learning model with neighborhood-level structural representation for predicting lncRNA-miRNA interactions.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Graph informed biomarker discovery framework using transcriptomic machine learning for glioblastoma prognosis.Scientific reports · 2026Article
- Integrating multimodal features with deep learning for protein solubility prediction.Journal of cheminformatics · 2026Article
- MicroRNA target gene prediction model based on input-feature dependency and sample data expansion technique.PLoS computational biology · 2026Article
- Relation-aware context aggregation framework for sparse knowledge graph completion.Scientific reports · 2026Article
- A quantum graph-based differential model for drug-drug interaction prediction.Journal of computer-aided molecular design · 2026Article
- SSAS-GO: structure-sequence adaptive synergy network for protein function prediction.Briefings in bioinformatics · 2026Article
- Fine-tuning LLM hyperparameters to align semantic and physiological contexts of aging-related pathways.Molecular diversity · 2026Article
- Capsule enclosed coordinate attention based dual batch depthwise convolutional knowledge distillation model for drug-drug interaction prediction.Molecular diversity · 2026Article
- iGraphCTC: an inter-connected graph convolutional network for comprehensive clinical trial collaborations.Scientific reports · 2026Article
- ConvAHKG: Action-based hybrid knowledge graph with a dual-channel convolutional approach for drug repurposing.Scientific reports · 2026Article
- Therapeutic impact of Mongolian Andai therapy on gut microbiota and metabolomic profiles in depression.Frontiers in neuroscience · 2026Article
- Agentic AI for trustworthy synthetic microbial genomics: a perspective on generation, validation, and governance.Frontiers in bioinformatics · 2026Article
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8 authors.
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Abstract
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are closely related to the treatment of human diseases. Traditional biological experiments often require time-consuming and labor-intensive in their search for mechanisms of disease. Computational methods are regarded as an effective way to predict unknown lncRNA-miRNA interactions (LMIs). However, most of them complete their tasks by mainly focusing on a single lncRNA-miRNA network without considering the complex mechanism between biomolecular in life activities, which are believed to be useful for improving the accuracy of LMI prediction. To address this, a heterogeneous information network (HIN) learning model with neighborhood-level structural representation, called HINLMI, to precisely identify LMIs. In particular, HINLMI first constructs a HIN by integrating nine interactions of five biomolecules. After that, different representation learning strategies are applied to learn the biological and network representations of lncRNAs and miRNAs in the HIN from different perspectives. Finally, HINLMI incorporates the XGBoost classifier to predict unknown LMIs using final embeddings of lncRNAs and miRNAs. Experimental results show that HINLMI yields a best performance on the real dataset when compared with state-of-the-art computational models. Moreover, several analysis experiments indicate that the simultaneous consideration of biological knowledge and network topology of lncRNAs and miRNAs allows HINLMI to accurately predict LMIs from a more comprehensive perspective. The promising performance of HINLMI also reveals that the utilization of rich heterogeneous information can provide an alternative insight for HINLMI to identify novel interactions between lncRNAs and miRNAs.
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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.