ArticleBMC bioinformatics2018
Measuring phenotype-phenotype similarity through the interactome.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- The effect of government information release on public protective behaviors during the emerging infectious diseases epidemic in China.Frontiers in public health · 2026Article
- A graph theoretical approach to experimental prioritization in genome-scale investigations.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.PloS one · 2023Article
- Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View.Genes · 2022Review
- FAIR data representation in times of eScience: a comparison of instance-based and class-based semantic representations of empirical data using phenotype descriptions as example.Journal of biomedical semantics · 2021Article
- Predicting candidate genes from phenotypes, functions and anatomical site of expression.Bioinformatics (Oxford, England) · 2021Article
- Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases.PLoS genetics · 2020Article
- Synthetic repurposing of drugs against hypertension: a datamining method based on association rules and a novel discrete algorithm.BMC bioinformatics · 2020Article
- ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function.BMC bioinformatics · 2019Article
- Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders.BMC bioinformatics · 2019Article
- Prioritizing candidate diseases-related metabolites based on literature and functional similarity.BMC bioinformatics · 2019Article
- TS-GOEA: a web tool for tissue-specific gene set enrichment analysis based on gene ontology.BMC bioinformatics · 2019Article
- Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.BMC bioinformatics · 2019Article
- Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO.BMC systems biology · 2019Article
- k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification.Frontiers in genetics · 2019Article
- Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder.Frontiers in genetics · 2019Article
- Exposing the Causal Effect of Body Mass Index on the Risk of Type 2 Diabetes Mellitus: A Mendelian Randomization Study.Frontiers in genetics · 2019Article
- Computational drug repositioning using meta-path-based semantic network analysis.BMC systems biology · 2018Article
- Article
- Exposing the Causal Effect of C-Reactive Protein on the Risk of Type 2 Diabetes Mellitus: A Mendelian Randomization Study.Frontiers in genetics · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecently, measuring phenotype similarity began to play an important role in disease diagnosis. Researchers have begun to pay attention to develop phenotype similarity measurement. However, existing methods ignore the interactions between phenotype-associated proteins, which may lead to inaccurate phenotype similarity.
resultsWe proposed a network-based method PhenoNet to calculate the similarity between phenotypes. We localized phenotypes in the network and calculated the similarity between phenotype-associated modules by modeling both the inter- and intra-similarity.
conclusionsPhenoNet was evaluated on two independent evaluation datasets: gene ontology and gene expression data. The result shows that PhenoNet performs better than the state-of-art methods on all evaluation tests.
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Registered trials
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 Socratic graph.