ArticleScientific reports2015
PhenomeExpress: a refined network analysis of expression datasets by inclusion of known disease phenotypes.
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 13 papers.
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Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Identification of phenotype-specific networks from paired gene expression-cell shape imaging data.Genome research · 2022Article
- Using Multilayer Heterogeneous Networks to Infer Functions of Phosphorylated Sites.Journal of proteome research · 2021Article
- NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Article
- Evaluation and Interpretation of Transcriptome Data Underlying Heterogeneous Chronic Obstructive Pulmonary Disease.Genomics & informatics · 2019Article
- The Potential Mechanism of Bufadienolide-Like Chemicals on Breast Cancer via Bioinformatics Analysis.Cancers · 2019Article
- Stratification of knee osteoarthritis: two major patient subgroups identified by genome-wide expression analysis of articular cartilage.Annals of the rheumatic diseases · 2018Article
- Article
- Systems approaches in osteoarthritis: Identifying routes to novel diagnostic and therapeutic strategies.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2017Article
- PhenomeScape: a cytoscape app to identify differentially regulated sub-networks using known disease associations.Bioinformatics (Oxford, England) · 2016Article
- Gene expression changes in damaged osteoarthritic cartilage identify a signature of non-chondrogenic and mechanical responses.Osteoarthritis and cartilage · 2016Article
- Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks.F1000Research · 2016Article
- Network-Based Enriched Gene Subnetwork Identification: A Game-Theoretic Approach.Biomedical engineering and computational biology · 2016Article
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Authors and funding
4 authors at 2 institutions in 1 country.
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Abstract
We describe a new method, PhenomeExpress, for the analysis of transcriptomic datasets to identify pathogenic disease mechanisms. Our analysis method includes input from both protein-protein interaction and phenotype similarity networks. This introduces valuable information from disease relevant phenotypes, which aids the identification of sub-networks that are significantly enriched in differentially expressed genes and are related to the disease relevant phenotypes. This contrasts with many active sub-network detection methods, which rely solely on protein-protein interaction networks derived from compounded data of many unrelated biological conditions and which are therefore not specific to the context of the experiment. PhenomeExpress thus exploits readily available animal model and human disease phenotype information. It combines this prior evidence of disease phenotypes with the experimentally derived disease data sets to provide a more targeted analysis. Two case studies, in subchondral bone in osteoarthritis and in Pax5 in acute lymphoblastic leukaemia, demonstrate that PhenomeExpress identifies core disease pathways in both mouse and human disease expression datasets derived from different technologies. We also validate the approach by comparison to state-of-the-art active sub-network detection methods, which reveals how it may enhance the detection of molecular phenotypes and provide a more detailed context to those previously identified as possible candidates.
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