Evidence map›Paper›PMID 28705239›Full record

ArticleBMC research notes2017

NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks.

Theodosios Theodosiou, Georgios Efstathiou, Nikolas Papanikolaou, Nikos C Kyrpides, Pantelis G Bagos, Ioannis Iliopoulos, Georgios A Pavlopoulos

Abstract read
In one paragraph

Article in BMC research notes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Biological Network Mining.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
  10. Article
  11. A Guide to Conquer the Biological Network Era Using Graph Theory.Frontiers in bioengineering and biotechnology · 2020
    Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Theodosios TheodosiouBioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete Medical School, 70013 Heraklion, Crete, Greece.
Georgios EfstathiouBioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete Medical School, 70013 Heraklion, Crete, Greece.
Nikolas PapanikolaouBioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete Medical School, 70013 Heraklion, Crete, Greece.
Nikos C KyrpidesJoint Genome Institute, Lawrence Berkeley Lab, United States Department of Energy, 2800 Mitchell Drive, Walnut Creek, CA, 94598, USA.
Pantelis G BagosDepartment of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, Galaneika, 35100, Lamia, Greece.
Ioannis IliopoulosBioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete Medical School, 70013 Heraklion, Crete, Greece. iliopj@med.uoc.gr.
Georgios A PavlopoulosBioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete Medical School, 70013 Heraklion, Crete, Greece. g.pavlopoulos@lbl.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveNowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks.

resultsNetwork Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .

Indexed as

Gene Regulatory NetworksInternetSignal TransductionSoftwareAlgorithmsCluster AnalysisUser-Computer InterfaceCentralitiesNetwork biologyNetwork comparisonNetwork topologyNode and edge ranking

Identifiers

PMID28705239
PMCPMC5513407

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.