ArticleBMC bioinformatics2021
SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- MCOAN: multimodal contrastive representation learning for cross-omics adaptive disease regulatory network prediction.Bioinformatics (Oxford, England) · 2026Article
- Revealing new associations between lncRNAs and diseases through cross attention mechanism and multiple level feature fusion.Scientific reports · 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
- Deep learning of multimodal networks with topological regularization for drug repositioning.Journal of cheminformatics · 2024Article
- GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations.BMC bioinformatics · 2024Article
- Finding potential lncRNA-disease associations using a boosting-based ensemble learning model.Frontiers in genetics · 2024Article
- Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network.PLoS computational biology · 2023Article
- lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs.Database : the journal of biological databases and curation · 2023Article
- Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM.Frontiers in genetics · 2023Article
- Predicting miRNA-disease associations based on graph attention network with multi-source information.BMC bioinformatics · 2022Article
- Hierarchical graph attention network for miRNA-disease association prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
- Discovering miRNAs Associated With Multiple Sclerosis Based on Network Representation Learning and Deep Learning Methods.Frontiers in genetics · 2022Article
- Prediction of potential small molecule-miRNA associations based on heterogeneous network representation learning.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundNumerous studies on discovering the roles of long non-coding RNAs (lncRNAs) in the occurrence, development and prognosis progresses of various human diseases have drawn substantial attentions. Since only a tiny portion of lncRNA-disease associations have been properly annotated, an increasing number of computational methods have been proposed for predicting potential lncRNA-disease associations. However, traditional predicting models lack the ability to precisely extract features of biomolecules, it is urgent to find a model which can identify potential lncRNA-disease associations with both efficiency and accuracy.
resultsIn this study, we proposed a novel model, SVDNVLDA, which gained the linear and non-linear features of lncRNAs and diseases with Singular Value Decomposition (SVD) and node2vec methods respectively. The integrated features were constructed from connecting the linear and non-linear features of each entity, which could effectively enhance the semantics contained in ultimate representations. And an XGBoost classifier was employed for identifying potential lncRNA-disease associations eventually.
conclusionsWe propose a novel model to predict lncRNA-disease associations. This model is expected to identify potential relationships between lncRNAs and diseases and further explore the disease mechanisms at the lncRNA molecular level.
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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.