iRDA : a new filter towards predictive, stable, and enriched candidate genes
- Submitting institution
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King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 87314893
- Type
- D - Journal article
- DOI
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10.1186/s12864-015-2129-5
- Title of journal
- BMC GENOMICS
- Article number
- 1041
- First page
- -
- Volume
- 16
- Issue
- -
- ISSN
- 1471-2164
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2015
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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2
- Research group(s)
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-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We present a new multivariate filter (iRDA) for HTS gene-expression candidate genes discovery which, in contrast to existing methods, allows a more complex analysis by taking implicit dependencies into consideration. This is achieved by combining approximate Markov blankets, information theory, and several novel heuristic search strategies. Its performance is evaluated comprehensively not just on one data set, but seven cancer benchmarks and four disease experiments, with a rigorous, in-depth comparison to three established filters. iRDA is shown to have a better classification performance than its competitors, e.g., up to 5% less error, and is consistently producing statistically more enriched sets.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -