ArticleBiology2014
R-based software for the integration of pathway data into bioinformatic algorithms.
Article in Biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Article
- The metaRbolomics Toolbox in Bioconductor and beyond.Metabolites · 2019Review
- Pathview Web: user friendly pathway visualization and data integration.Nucleic acids research · 2017Article
- Interdisciplinary approach towards a systems medicine toolbox using the example of inflammatory diseases.Briefings in bioinformatics · 2017Article
- PaxtoolsR: pathway analysis in R using Pathway Commons.Bioinformatics (Oxford, England) · 2016Article
- Building pathway graphs from BioPAX data in R.F1000Research · 2016Article
- Comparative study on gene set and pathway topology-based enrichment methods.BMC bioinformatics · 2015Article
- Newly Constructed Network Models of Different WNT Signaling Cascades Applied to Breast Cancer Expression Data.PloS one · 2015Article
- Improving collaboration by standardization efforts in systems biology.Frontiers in bioengineering and biotechnology · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Putting new findings into the context of available literature knowledge is one approach to deal with the surge of high-throughput data results. Furthermore, prior knowledge can increase the performance and stability of bioinformatic algorithms, for example, methods for network reconstruction. In this review, we examine software packages for the statistical computing framework R, which enable the integration of pathway data for further bioinformatic analyses. Different approaches to integrate and visualize pathway data are identified and packages are stratified concerning their features according to a number of different aspects: data import strategies, the extent of available data, dependencies on external tools, integration with further analysis steps and visualization options are considered. A total of 12 packages integrating pathway data are reviewed in this manuscript. These are supplemented by five R-specific packages for visualization and six connector packages, which provide access to external tools.
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.