Evidence map›Paper›PMID 23986498›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2013

Reconstructing dynamic microRNA-regulated interaction networks.

Marcel H Schulz, Kusum V Pandit, Christian L Lino Cardenas, Namasivayam Ambalavanan, Naftali Kaminski, Ziv Bar-Joseph

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed.

  1. Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026
    Review
  2. Article
  3. Review
  4. Temporal Expression Analysis to Unravel Gene Regulatory Dynamics by microRNAs.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Adipocyte · 2022
    Article
  9. miR-338-3p blocks TGFβ-induced myofibroblast differentiation through the induction of PTEN.American journal of physiology. Lung cellular and molecular physiology · 2022
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Integrating multiomics longitudinal data to reconstruct networks underlying lung development.American journal of physiology. Lung cellular and molecular physiology · 2019
    Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Marcel H SchulzRay and Stephanie Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.
Kusum V Pandit
Christian L Lino Cardenas
Namasivayam Ambalavanan
Naftali Kaminski
Ziv Bar-Joseph

Funding

Lung Genomics Research ConsortiumRC2HL101715 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI GERACI, MARK W, KAMINSKI, NAFTALI · 2009 to 2010
$11.4M
Molecular Phenotypes within and across disease boundaries in IPF and COPDR01HL095397 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KAMINSKI, NAFTALI, SHAPIRO, STEVEN D · 2008 to 2011
$3.0M
Gene networks beyond organ boundaries; heart, lung and pulmonary vascular diseaseU01HL108642 · NHLBI · YALE UNIVERSITY · PI AHMAD, FERHAAN, BENOS, PANAGIOTIS V · 2011 to 2014
$2.2M
TRANSFORMING GROWTH FACTOR - BETA MEDIATES EFFECTS OF HYPOXIA IN NEWBORN LUNGR01HL092906 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI AMBALAVANAN, NAMASIVAYAM · 2008 to 2012
$1.9M
Reconstructing dynamic regulatory networksR01GM085022 · NIGMS · CARNEGIE-MELLON UNIVERSITY · PI BAR-JOSEPH, ZIV · 2008 to 2011
$1.1M
NHLBI NIH HHS R01 HL092906NHLBI NIH HHS R01 HL095397NHLBI NIH HHS R01HL095397NHLBI NIH HHS RC2 HL101715NHLBI NIH HHS RC2HL101715NHLBI NIH HHS U01 HL108642NHLBI NIH HHS U01HL108642NIGMS NIH HHS 1R01 GM085022NIGMS NIH HHS R01 GM085022
6 · The paper itself

Abstract

The regulation of gene expression in cells, including by microRNAs (miRNAs), is a dynamic process. Current methods for identifying miRNA targets by combining sequence and miRNA and mRNA expression data do not adequately use the temporal information and thus miss important miRNAs and their targets. We developed the MIRna Dynamic Regulatory Events Miner (mirDREM), a probabilistic modeling method that uses input-output hidden Markov models to reconstruct dynamic regulatory networks that explain how temporal gene expression is jointly regulated by miRNAs and transcription factors. We measured miRNA and mRNA expression for postnatal lung development in mice and used mirDREM to study the regulation of this process. The reconstructed dynamic network correctly identified known miRNAs and transcription factors. The method has also provided predictions about additional miRNAs regulating this process and the specific developmental phases they regulate, several of which were experimentally validated. Our analysis uncovered links between miRNAs involved in lung development and differentially expressed miRNAs in idiopathic pulmonary fibrosis patients, some of which we have experimentally validated using proliferation assays. These results indicate that some disease progression pathways in idiopathic pulmonary fibrosis may represent partial reversal of lung differentiation.

Indexed as

Gene Expression RegulationGene Regulatory NetworksAlgorithmsAnimalsCell ProliferationGene Knockdown TechniquesHumansIdiopathic Pulmonary FibrosisLungMiceMicroRNAsModels, GeneticReproducibility of ResultsRNA, MessengerMicroRNAsRNA, Messengernetwork modelingsystems biology

Identifiers

PMID23986498
PMCPMC3785769

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.