ArticleBMC genomics2015
Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 2 of them syntheses that pooled it.
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Who cites it
67 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Model-based multifacet clustering with high-dimensional omics applications.Biostatistics (Oxford, England) · 2025Article
- Mitochondrial components secretion in extracellular vesicles promotes alveolar epithelial mitochondrial quality control.Nature communications · 2025Article
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- Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis.Orphanet journal of rare diseases · 2025Article
- Aging-associated alterations in gene regulatory networks associate with risk, prognosis and response to therapy in lung adenocarcinoma.npj aging · 2025Article
- Let-7 restrains an epigenetic circuit in AT2 cells to prevent fibrogenic intermediates in pulmonary fibrosis.Nature communications · 2025Article
- An integrated machine learning model of transcriptomic genes in multi-center chronic obstructive pulmonary disease reveals the causal role of TIMP4 in airway epithelial cell.Respiratory research · 2025Article
- Immunological Features and Potential Biomarkers of Systemic Sclerosis-Associated Interstitial Lung Disease and Idiopathic Pulmonary Fibrosis.The clinical respiratory journal · 2025Article
- Integrating bioinformatics and molecular experiments to reveal the critical role of the cellular energy metabolism-related marker PLA2G1B in COPD epithelial cells.Frontiers in immunology · 2025Article
- GTSE1-driven ZEB1 stabilization promotes pulmonary fibrosis through the epithelial-to-mesenchymal transition.Molecular therapy : the journal of the American Society of Gene Therapy · 2024Article
- Identifications of Similarity Metrics for Patients With Cancer: Protocol for a Scoping Review.JMIR research protocols · 2024Article
- Gene regulatory networks reveal sex difference in lung adenocarcinoma.Biology of sex differences · 2024Article
7 more citing papers are in PubMed but not listed here.
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9 authors.
Funding
Abstract
backgroundThe increased multi-omics information on carefully phenotyped patients in studies of complex diseases requires novel methods for data integration. Unlike continuous intensity measurements from most omics data sets, phenome data contain clinical variables that are binary, ordinal and categorical.
resultsIn this paper we introduce an integrative phenotyping framework (iPF) for disease subtype discovery. A feature topology plot was developed for effective dimension reduction and visualization of multi-omics data. The approach is free of model assumption and robust to data noises or missingness. We developed a workflow to integrate homogeneous patient clustering from different omics data in an agglomerative manner and then visualized heterogeneous clustering of pairwise omics sources. We applied the framework to two batches of lung samples obtained from patients diagnosed with chronic obstructive lung disease (COPD) or interstitial lung disease (ILD) with well-characterized clinical (phenomic) data, mRNA and microRNA expression profiles. Application of iPF to the first training batch identified clusters of patients consisting of homogenous disease phenotypes as well as clusters with intermediate disease characteristics. Analysis of the second batch revealed a similar data structure, confirming the presence of intermediate clusters. Genes in the intermediate clusters were enriched with inflammatory and immune functional annotations, suggesting that they represent mechanistically distinct disease subphenotypes that may response to immunomodulatory therapies. The iPF software package and all source codes are publicly available.
conclusionsIdentification of subclusters with distinct clinical and biomolecular characteristics suggests that integration of phenomic and other omics information could lead to identification of novel mechanism-based disease sub-phenotypes.
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