ArticleOncotarget2016
IntNetLncSim: an integrative network analysis method to infer human lncRNA functional similarity.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Neighborhood based computational approaches for the prediction of lncRNA-disease associations.BMC bioinformatics · 2024Article
- HNetGO: protein function prediction via heterogeneous network transformer.Briefings in bioinformatics · 2023Article
- Review
- Identification ofComputational and mathematical methods in medicine · 2022Article
- Identifying lncRNA-disease association based on GAT multiple-operator aggregation and inductive matrix completion.Frontiers in genetics · 2022Article
- Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease.Frontiers in physiology · 2021Article
- Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview.Frontiers in genetics · 2021Review
- Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D.Frontiers in genetics · 2021Article
- Identification of miRNA Signature Associated With Erectile Dysfunction in Type 2 Diabetes Mellitus by Support Vector Machine-Recursive Feature Elimination.Frontiers in genetics · 2021Article
- Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes.Frontiers in genetics · 2021Article
- Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm.Frontiers in genetics · 2021Review
- A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem.Frontiers in genetics · 2021Article
- Comprehensive Analysis Revealed thatFrontiers in cell and developmental biology · 2021Article
- Application of Sparse Representation in Bioinformatics.Frontiers in genetics · 2021Review
- Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets.Molecular therapy. Nucleic acids · 2020Article
- IDSSIM: an lncRNA functional similarity calculation model based on an improved disease semantic similarity method.BMC bioinformatics · 2020Article
- A random forest based computational model for predicting novel lncRNA-disease associations.BMC bioinformatics · 2020Article
- LDAI-ISPS: LncRNA-Disease Associations Inference Based on Integrated Space Projection Scores.International journal of molecular sciences · 2020Article
- Identification and Validation of Novel Long Non-coding RNA Biomarkers for Early Diagnosis of Oral Squamous Cell Carcinoma.Frontiers in bioengineering and biotechnology · 2020Article
- Inferring lncRNA Functional Similarity Based on Integrating Heterogeneous Network Data.Frontiers in bioengineering and biotechnology · 2020Article
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8 authors.
Funding
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Abstract
Increasing evidence indicated that long non-coding RNAs (lncRNAs) were involved in various biological processes and complex diseases by communicating with mRNAs/miRNAs each other. Exploiting interactions between lncRNAs and mRNA/miRNAs to lncRNA functional similarity (LFS) is an effective method to explore function of lncRNAs and predict novel lncRNA-disease associations. In this article, we proposed an integrative framework, IntNetLncSim, to infer LFS by modeling the information flow in an integrated network that comprises both lncRNA-related transcriptional and post-transcriptional information. The performance of IntNetLncSim was evaluated by investigating the relationship of LFS with the similarity of lncRNA-related mRNA sets (LmRSets) and miRNA sets (LmiRSets). As a result, LFS by IntNetLncSim was significant positively correlated with the LmRSet (Pearson correlation γ2=0.8424) and LmiRSet (Pearson correlation γ2=0.2601). Particularly, the performance of IntNetLncSim is superior to several previous methods. In the case of applying the LFS to identify novel lncRNA-disease relationships, we achieved an area under the ROC curve (0.7300) in experimentally verified lncRNA-disease associations based on leave-one-out cross-validation. Furthermore, highly-ranked lncRNA-disease associations confirmed by literature mining demonstrated the excellent performance of IntNetLncSim. Finally, a web-accessible system was provided for querying LFS and potential lncRNA-disease relationships: http://www.bio-bigdata.com/IntNetLncSim.
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