Short co-occurring polypeptide regions can predict global protein interaction maps
Scientific Reports, ISSN: 2045-2322, Vol: 2, Issue: 1, Page: 239
2012
- 42Citations
- 66Captures
- 2Mentions
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
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.
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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
- Citations42
- Citation Indexes42
- 42
- CrossRef40
- Captures66
- Readers66
- 66
- Mentions2
- References2
- Wikipedia2
Article Description
A goal of the post-genomics era has been to elucidate a detailed global map of protein-protein interactions (PPIs) within a cell. Here, we show that the presence of co-occurring short polypeptide sequences between interacting protein partners appears to be conserved across different organisms. We present an algorithm to automatically generate PPI prediction method parameters for various organisms and illustrate that global PPIs can be predicted from previously reported PPIs within the same or a different organism using protein primary sequences. The PPI prediction code is further accelerated through the use of parallel multi-core programming, which improves its usability for large scale or proteome-wide PPI prediction. We predict and analyze hundreds of novel human PPIs, experimentally confirm protein functions and importantly predict the first genome-wide PPI maps for S. pombe (∼1/49,000 PPIs) and C. elegans (∼1/437,500 PPIs).
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84859771604&origin=inward; http://dx.doi.org/10.1038/srep00239; http://www.ncbi.nlm.nih.gov/pubmed/22355752; https://www.nature.com/articles/srep00239; https://dx.doi.org/10.1038/srep00239; http://www.nature.com/srep/2012/120130/srep00239/full/srep00239.html; http://www.nature.com/doifinder/10.1038/srep00239; http://www.nature.com/articles/srep00239; http://www.nature.com/articles/srep00239.pdf
Springer Science and Business Media LLC
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