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NFPscanner: a webtool for knowledge-based deciphering of biomedical networks

Overview of attention for article published in BMC Bioinformatics, May 2017
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Title
NFPscanner: a webtool for knowledge-based deciphering of biomedical networks
Published in
BMC Bioinformatics, May 2017
DOI 10.1186/s12859-017-1673-1
Pubmed ID
Authors

Wenjian Xu, Yang Cao, Ziwei Xie, Haochen He, Song He, Hao Hong, Xiaochen Bo, Fei Li

Abstract

Many biological pathways have been created to represent different types of knowledge, such as genetic interactions, metabolic reactions, and gene-regulating and physical-binding relationships. Biologists are using a wide range of omics data to elaborately construct various context-specific differential molecular networks. However, they cannot easily gain insight into unfamiliar gene networks with the tools that are currently available for pathways resource and network analysis. They would benefit from the development of a standardized tool to compare functions of multiple biological networks quantitatively and promptly. To address this challenge, we developed NFPscanner, a web server for deciphering gene networks with pathway associations. Adapted from a recently reported knowledge-based framework called network fingerprint, NFPscanner integrates the annotated pathways of 7 databases, 4 algorithms, and 2 graphical visualization modules into a webtool. It implements 3 types of network analysis: Fingerprint: Deciphering gene networks and highlighting inherent pathway modules Alignment: Discovering functional associations by finding optimized node mapping between 2 gene networks Enrichment: Calculating and visualizing gene ontology (GO) and pathway enrichment for genes in networks Users can upload gene networks to NFPscanner through the web interface and then interactively explore the networks' functions. NFPscanner is open-source software for non-commercial use, freely accessible at http://biotech.bmi.ac.cn/nfs .

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 4%
Unknown 24 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 32%
Student > Master 4 16%
Researcher 4 16%
Student > Bachelor 3 12%
Professor 2 8%
Other 3 12%
Unknown 1 4%
Readers by discipline Count As %
Computer Science 10 40%
Agricultural and Biological Sciences 4 16%
Medicine and Dentistry 3 12%
Engineering 2 8%
Energy 1 4%
Other 3 12%
Unknown 2 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 October 2017.
All research outputs
#15,770,855
of 17,841,093 outputs
Outputs from BMC Bioinformatics
#5,874
of 6,288 outputs
Outputs of similar age
#235,138
of 275,556 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 11 outputs
Altmetric has tracked 17,841,093 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,288 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.