Towards the Discovery of Reliable Biomarkers from Gene-Expression Profiles: An Iterative Constraint Satisfaction Learning Approach
Artificial Intelligence: Theories, Models and Applications, ISSN: 0302-9743, Page: 233-242
2010
- 1Captures
Metric Options: CountsSelecting 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.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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
- Captures1
- Readers1
Book Chapter Description
Artificial Intelligence: Theories, Models and Applications
Bibliographic Details
http://link.springer.com/10.1007/978-3-642-12842-4_27; http://link.springer.com/content/pdf/10.1007/978-3-642-12842-4_27.pdf; http://dx.doi.org/10.1007/978-3-642-12842-4_27; http://www.springerlink.com/index/10.1007/978-3-642-12842-4_27; http://www.springerlink.com/index/pdf/10.1007/978-3-642-12842-4_27
Springer Science and Business Media LLC
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