ArticleBMC medical genomics2017
Measuring disease similarity and predicting disease-related ncRNAs by a novel method.
Article in BMC medical genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 48 citations in OpenAlex.
- Contrastive hypergraph collaborative filtering for transfer RNA-disease association prediction.Briefings in bioinformatics · 2025Article
- DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm.International journal of molecular sciences · 2024Article
- Finding potential lncRNA-disease associations using a boosting-based ensemble learning model.Frontiers in genetics · 2024Article
- Recognition of outer membrane proteins using multiple feature fusion.Frontiers in genetics · 2023Article
- The microbiome types of colorectal tissue are potentially associated with the prognosis of patients with colorectal cancer.Frontiers in microbiology · 2023Article
- LDNFSGB: prediction of long non-coding rna and disease association using network feature similarity and gradient boosting.BMC bioinformatics · 2020Article
- LDAI-ISPS: LncRNA-Disease Associations Inference Based on Integrated Space Projection Scores.International journal of molecular sciences · 2020Article
- Context-Specific Coordinately Regulatory Network Prioritize Breast Cancer Genetic Risk Factors.Frontiers in genetics · 2020Article
- ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function.BMC bioinformatics · 2019Article
- Construction of Disease Similarity Networks Using Concept Embedding and Ontology.Studies in health technology and informatics · 2019Article
- Prediction of Potential Disease-Associated MicroRNAs by Using Neural Networks.Molecular therapy. Nucleic acids · 2019Article
- An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations.Frontiers in genetics · 2019Article
- PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2018Article
- Article
- Effective norm emergence in cell systems under limited communication.BMC bioinformatics · 2018Article
- BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data.BMC bioinformatics · 2018Article
- Measuring phenotype-phenotype similarity through the interactome.BMC bioinformatics · 2018Article
- Identifying diseases-related metabolites using random walk.BMC bioinformatics · 2018Article
- Automatic infection detection based on electronic medical records.BMC bioinformatics · 2018Article
- Higher-order partial least squares for predicting gene expression levels from chromatin states.BMC bioinformatics · 2018Article
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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
backgroundSimilar diseases are always caused by similar molecular origins, such as diasease-related protein-coding genes (PCGs). And the molecular associations reflect their similarity. Therefore, current methods for calculating disease similarity often utilized functional interactions of PCGs. Besides, the existing methods have neglected a fact that genes could also be associated in the gene functional network (GFN) based on intermediate nodes.
methodsHere we presented a novel method, InfDisSim, to deduce the similarity of diseases. InfDisSim utilized the whole network based on random walk with damping to model the information flow. A benchmark set of similar disease pairs was employed to evaluate the performance of InfDisSim.
resultsThe region beneath the receiver operating characteristic curve (AUC) was calculated to assess the performance. As a result, InfDisSim reaches a high AUC (0.9786) which indicates a very good performance. Furthermore, after calculating the disease similarity by the InfDisSim, we reconfirmed that similar diseases tend to have common therapeutic drugs (Pearson correlation γ
conclusionsThe high AUC based on benchmark data indicates the method performs well. The method is valuable in the prediction of disease-related 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.