Querying Microarray Databases
2005
- 11Usage
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
- Usage11
- Downloads11
Thesis / Dissertation Description
Microarray technology has rapidly taken a key position among bioinformatics research tools. After the completion of the Human Genome Project, microarray databases have become particularly important to the management and analysis of genomic data. These databases are ideal tools for many research areas involving gene expression patterns under different experimental conditions. This work attempts to assess the querying capabilities of current public microarray database implementations by evaluating their data management, query interfaces, and results presentation. We are not aware of any comparative study available to date that evaluates this important class of biological databases. We examine and evaluate how several of the current existing implementations handle microarray data so that they can be queried and managed in a useful, understandable, and efficient manner. Our study identifies some of the limitations among existing microarray databases that impact querying and results presentation, leading to suggestions for areas of improvement.
Bibliographic Details
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