- Title
- A new combinatorial optimization approach for integrated feature selection using different datasets: a prostate cancer transcriptomic study
- Creator
- Puthiyedth, Nisha; Riveros, Carlos; Berretta, Regina; Moscato, Pablo
- Relation
- ARC.DP120102576 http://purl.org/au-research/grants/arc/DP120102576
- Relation
- PLoS One Vol. 10, Issue 6
- Publisher Link
- http://dx.doi.org/10.1371/journal.pone.0127702
- Publisher
- Public Library of Science (PLOS) One
- Resource Type
- journal article
- Date
- 2015
- Description
- The joint study of multiple datasets has become a common technique for increasing statistical power in detecting biomarkers obtained from smaller studies. The approach generally followed is based on the fact that as the total number of samples increases, we expect to have greater power to detect associations of interest. This methodology has been applied to genome-wide association and transcriptomic studies due to the availability of datasets in the public domain. While this approach is well established in biostatistics, the introduction of new combinatorial optimization models to address this issue has not been explored in depth. In this study, we introduce a new model for the integration of multiple datasets and we show its application in transcriptomics.
- Subject
- multiple datasets; combinatorial optimization; multi-platform data integration; health; prostate cancer
- Identifier
- http://hdl.handle.net/1959.13/1321761
- Identifier
- uon:24445
- Identifier
- ISSN:1932-6203
- Rights
- © 2015 Puthiyedth et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
- Language
- eng
- Full Text
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