ArticleFrontiers in plant science2012
Correlation networks visualization.
Article in Frontiers in plant science, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Transcriptomic Insight into the Pollen Tube Growth ofPlants (Basel, Switzerland) · 2023Article
- Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom.Plants (Basel, Switzerland) · 2022Review
- Divergent Patterns of Bacterial Community Structure and Function in Response to Estuarine Output in the Middle of the Bohai Sea.Frontiers in microbiology · 2021Article
- Plant Bio-Wars: Maize Protein Networks Reveal Tissue-Specific Defense Strategies in Response to a Root Herbivore.Journal of chemical ecology · 2018Article
- Article
- Diurnal Transcriptome and Gene Network Represented through Sparse Modeling inFrontiers in plant science · 2017Article
- Chilling acclimation provides immunity to stress by altering regulatory networks and inducing genes with protective functions in cassava.BMC plant biology · 2014Article
- Review
- Integrated network analysis and effective tools in plant systems biology.Frontiers in plant science · 2014Review
- The plant transcriptome-from integrating observations to models.Frontiers in plant science · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New, in silico ways of generating hypotheses based on large data sets have emerged in the past decade. These data sets have been used to investigate different aspects of plant biology, especially at the level of transcriptome, from tissue-specific expression patterns to patterns in as little as a few cells. Such publicly available data are a boon to researchers for hypothesis generation by providing a guide for experimental work such as phenotyping or genetic analysis. More advanced computational methods can leverage these data via gene coexpression analysis, the results of which can be visualized and refined using network analysis. Other kinds of networks of, e.g., protein-protein interactions, can also be used to inform biology. These networks can be visualized and analyzed with additional information on gene expression levels, subcellular localization, etc., or with other emerging kinds information. Finally, cross-level correlation is an area that will become increasingly important. Visualizing these cross-level correlations will require new data visualization tools.
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.