The Quality Preserving Database: A Computational Framework for Encouraging Collaboration, Enhancing Power and Controlling False Discovery
| Autor: | Saharon Rosset, Ehud Aharoni, Hani Neuvirth |
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| Rok vydání: | 2011 |
| Předmět: |
Scheme (programming language)
Biomedical Research Computer science media_common.quotation_subject Control (management) Context (language use) computer.software_genre Machine learning symbols.namesake Genetics Quality (business) Statistical hypothesis testing computer.programming_language media_common Database business.industry Applied Mathematics Computational Biology Bonferroni correction Data Interpretation Statistical Bounded function Multiple comparisons problem symbols Database Management Systems Data mining Artificial intelligence business computer Biotechnology |
| Zdroj: | IEEE/ACM Transactions on Computational Biology and Bioinformatics. 8:1431-1437 |
| ISSN: | 1545-5963 |
| DOI: | 10.1109/tcbb.2010.105 |
| Popis: | The common scenario in computational biology in which a community of researchers conduct multiple statistical tests on one shared database gives rise to the multiple hypothesis testing problem. Conventional procedures for solving this problem control the probability of false discovery by sacrificing some of the power of the tests. We suggest a scheme for controlling false discovery without any power loss by adding new samples for each use of the database and charging the user with the expenses. The crux of the scheme is a carefully crafted pricing system that fairly prices different user requests based on their demands while keeping the probability of false discovery bounded. We demonstrate this idea in the context of HIV treatment research, where multiple researchers conduct tests on a repository of HIV samples. |
| Databáze: | OpenAIRE |
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