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A Robust Rerank Approach for Feature Selection and Its Application to Pooling-Based GWA Studies
Joint Authors
Liu, Jia-Rou
Kuo, Po-Hsiu
Hung, Hung
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-04-04
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Large-p-small-n datasets are commonly encountered in modern biomedical studies.
To detect the difference between two groups, conventional methods would fail to apply due to the instability in estimating variances in t-test and a high proportion of tied values in AUC (area under the receiver operating characteristic curve) estimates.
The significance analysis of microarrays (SAM) may also not be satisfactory, since its performance is sensitive to the tuning parameter, and its selection is not straightforward.
In this work, we propose a robust rerank approach to overcome the above-mentioned diffculties.
In particular, we obtain a rank-based statistic for each feature based on the concept of “rank-over-variable.” Techniques of “random subset” and “rerank” are then iteratively applied to rank features, and the leading features will be selected for further studies.
The proposed re-rank approach is especially applicable for large-p-small-n datasets.
Moreover, it is insensitive to the selection of tuning parameters, which is an appealing property for practical implementation.
Simulation studies and real data analysis of pooling-based genome wide association (GWA) studies demonstrate the usefulness of our method.
American Psychological Association (APA)
Liu, Jia-Rou& Kuo, Po-Hsiu& Hung, Hung. 2013. A Robust Rerank Approach for Feature Selection and Its Application to Pooling-Based GWA Studies. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-504113
Modern Language Association (MLA)
Liu, Jia-Rou…[et al.]. A Robust Rerank Approach for Feature Selection and Its Application to Pooling-Based GWA Studies. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-504113
American Medical Association (AMA)
Liu, Jia-Rou& Kuo, Po-Hsiu& Hung, Hung. A Robust Rerank Approach for Feature Selection and Its Application to Pooling-Based GWA Studies. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-504113
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-504113