MaxLink: Network-based prioritization of genes tightly linked to a disease seed set
Bioinformatics, ISSN: 1460-2059, Vol: 30, Issue: 18, Page: 2689-2690
2014
- 29Citations
- 51Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations29
- Citation Indexes29
- 29
- CrossRef23
- Captures51
- Readers51
- 51
Article Description
MaxLink, a guilt-by-association network search algorithm, has been made available as a web resource and a stand-alone version. Based on a user-supplied list of query genes, MaxLink identifies and ranks genes that are tightly linked to the query list. This functionality can be used to predict potential disease genes from an initial set of genes with known association to a disease. The original algorithm, used to identify and rank novel genes potentially involved in cancer, has been updated to use a more statistically sound method for selection of candidate genes and made applicable to other areas than cancer. The algorithm has also been made faster by re-implementation in C++, and the Web site uses FunCoup 3.0 as the underlying network. Availability and implementation: MaxLink is freely available at http://maxlink.sbc.su.se both as a web service and a stand-alone application for download.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84907485965&origin=inward; http://dx.doi.org/10.1093/bioinformatics/btu344; http://www.ncbi.nlm.nih.gov/pubmed/24849579; https://academic.oup.com/bioinformatics/article/30/18/2689/2475609; https://dx.doi.org/10.1093/bioinformatics/btu344
Oxford University Press (OUP)
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