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One of the main goals of metabolomics studies is to discover informative metabolites or biomarkers, which may be used to diagnose diseases and to find out pathology. Sophisticated feature selection approaches are required to extract the information hidden in such complex ‘omics’ data. In this study, it is proposed a new and robust selective method by combining random forests (RF) with model population...
In the structure–activity relationship (SAR) study, a learning algorithm is usually faced with the problem of selecting a compact subset of descriptors related to the property of interest, while ignoring the rest. This paper presents a new method of molecular descriptor selection utilizing three commonly used decision tree (DT)-based ensemble methods coupled with a backward elimination strategy (BES)...
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