ArticlePLoS computational biology2015
Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Fast skeletal myosin binding protein-C expression exacerbates dysfunction in heart failure.Basic research in cardiology · 2026Article
- LncRNA Foxo6os as a Novel " Scaffold" Mediates MYBPC3 in Combating Pathological Cardiac Hypertrophy and Heart Failure.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer.Computational and structural biotechnology journal · 2025Article
- Computational systems biology in disease modeling and control, review and perspectives.NPJ systems biology and applications · 2022Review
- Article
- Analysis of multiple gene co-expression networks to discover interactions favoring CFTR biogenesis and ΔF508-CFTR rescue.BMC medical genomics · 2021Article
- TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain.BMC bioinformatics · 2021Article
- Maternal obesity persistently alters cardiac progenitor gene expression and programs adult-onset heart disease susceptibility.Molecular metabolism · 2021Article
- Predicting viral exposure response from modeling the changes of co-expression networks using time series gene expression data.BMC bioinformatics · 2020Article
- An integrative module analysis of DNA methylation landscape in aging.Experimental and therapeutic medicine · 2019Article
- Differential network as an indicator of osteoporosis with network entropy.Experimental and therapeutic medicine · 2018Article
- Regularized Multi-View Subspace Clustering for Common Modules Across Cancer Stages.Molecules (Basel, Switzerland) · 2018Article
- Identification of functional modules induced by bare-metal stents and paclitaxel-eluting stents in coronary heart disease.Experimental and therapeutic medicine · 2018Article
- Differential gene regulatory networks in development and disease.Cellular and molecular life sciences : CMLS · 2018Review
- Silymarin-mediated regulation of the cell cycle and DNA damage response exerts antitumor activity in human hepatocellular carcinoma.Oncology letters · 2018Article
- Identifying pathway modules of tuberculosis in children by analyzing multiple different networks.Experimental and therapeutic medicine · 2018Article
- Multi-Objective Optimization Algorithm to Discover Condition-Specific Modules in Multiple Networks.Molecules (Basel, Switzerland) · 2017Article
- Dynamical differential networks and modules inferring disrupted genes associated with the progression of Alzheimer's disease.Experimental and therapeutic medicine · 2017Article
- Modeling osteosarcoma progression by measuring the connectivity dynamics using an inference of multiple differential modules algorithm.Molecular medicine reports · 2017Article
- A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation.PLoS computational biology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the iMDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (multiple differential modules). We applied iMDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that iMDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. iMDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.