Comput Math Methods Med - A robust rerank approach for feature selection and its application to pooling-based GWA studies.

Tópicos

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Resumo

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.

Resumo Limpo

largepsmalln dataset common encount modern biomed studi detect differ two group convent method fail appli due instabl estim varianc ttest high proport tie valu auc area receiv oper characterist curv estim signific analysi microarray sam may also satisfactori sinc perform sensit tune paramet select straightforward work propos robust rerank approach overcom abovement diffculti particular obtain rankbas statist featur base concept rankovervari techniqu random subset rerank iter appli rank featur lead featur will select studi propos rerank approach especi applic largepsmalln dataset moreov insensit select tune paramet appeal properti practic implement simul studi real data analysi poolingbas genom wide associ gwa studi demonstr use method

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