ArticleScientific reports2015
Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers.
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Who cites it
111 citing papers in PubMed, 251 citations in OpenAlex.
- Computational Method Using Attribute-Aware Message Passing and Graph Convolutional Network for Potential miRNA-Disease Association Prediction.International journal of molecular sciences · 2026Article
- Multi-granularity transformer contrastive learning and feature reconstruction for prediction of disease-related miRNAs.BMC bioinformatics · 2026Article
- Hypergraph Learning with Hyperedge Gating and Multiscale Topology Feature Learning for Predicting Disease-Related circRNAs.ACS omega · 2025Article
- Predicting Potential Microbe-Disease Associations Based on Heterogeneous Graph Random Attention Neural Network and Neural Collaborative Filtering.Interdisciplinary sciences, computational life sciences · 2025Article
- LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting.BMC bioinformatics · 2025Article
- Dual balanced augmented topological noncoding RNA disease triplet association in heterogeneous graphs.Briefings in bioinformatics · 2025Article
- MVGNCDA: Identifying Potential circRNA-Disease Associations Based on Multi-view Graph Convolutional Networks and Network Embeddings.Interdisciplinary sciences, computational life sciences · 2025Article
- HGCMLDA: predicting lncRNA-disease associations using hypergraph contrastive learning and multi-scale attentional feature fusion.Briefings in bioinformatics · 2025Article
- iGATTLDA: Integrative graph attention and transformer-based model for predicting lncRNA-Disease associations.IET systems biology · 2024Article
- ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs.Briefings in bioinformatics · 2024Article
- A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers.Briefings in bioinformatics · 2024Article
- Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction.BMC genomics · 2024Article
- GCN-Based Heterogeneous Complex Feature Learning to Enhance Predictability for LncRNA-Disease Associations.ACS omega · 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
- LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.Briefings in bioinformatics · 2023Article
- Association filtering and generative adversarial networks for predicting lncRNA-associated disease.BMC bioinformatics · 2023Article
- ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network.BMC genomics · 2023Article
- Upregulation of the c-MYC oncogene and adjacent long noncoding RNAs PVT1 and CCAT1 in esophageal squamous cell carcinoma.BMC cancer · 2023Article
- Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction.Frontiers in genetics · 2023Article
51 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasing evidence has indicated that plenty of lncRNAs play important roles in many critical biological processes. Developing powerful computational models to construct lncRNA functional similarity network based on heterogeneous biological datasets is one of the most important and popular topics in the fields of both lncRNAs and complex diseases. Functional similarity network construction could benefit the model development for both lncRNA function inference and lncRNA-disease association identification. However, little effort has been attempted to analysis and calculate lncRNA functional similarity on a large scale. In this study, based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases, we developed two novel lncRNA functional similarity calculation models (LNCSIM). LNCSIM was evaluated by introducing similarity scores into the model of Laplacian Regularized Least Squares for LncRNA-Disease Association (LRLSLDA) for lncRNA-disease association prediction. As a result, new predictive models improved the performance of LRLSLDA in the leave-one-out cross validation of various known lncRNA-disease associations datasets. Furthermore, some of the predictive results for colorectal cancer and lung cancer were verified by independent biological experimental studies. It is anticipated that LNCSIM could be a useful and important biological tool for human disease diagnosis, treatment, and prevention.
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