Support vector machine for predicting protein interactions using domain scores
Journal of Shanghai University, ISSN: 1007-6417, Vol: 13, Issue: 3, Page: 207-212
2009
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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.
Article Description
Protein-protein interactions play a crucial role in the cellular process such as metabolic pathways and immunological recognition. This paper presents a new domain score-based support vector machine (SVM) to infer protein interactions, which can be used not only to explore all possible domain interactions by the kernel method, but also to reflect the evolutionary conservation of domains in proteins by using the domain scores of proteins. The experimental result on the Saccharomyces cerevisiae dataset demonstrates that this approach can predict protein-protein interactions with higher performances compared to the existing approaches. © 2009 Shanghai University and Springer-Verlag GmbH.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=67650473032&origin=inward; http://dx.doi.org/10.1007/s11741-009-0303-2; http://link.springer.com/10.1007/s11741-009-0303-2; http://www.springerlink.com/index/10.1007/s11741-009-0303-2; http://www.springerlink.com/index/pdf/10.1007/s11741-009-0303-2; https://dx.doi.org/10.1007/s11741-009-0303-2; https://link.springer.com/article/10.1007/s11741-009-0303-2
Springer Science and Business Media LLC
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