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|Title:||Biomarker selection and classification of "- Omics " data using a two-step bayes classification framework|
Philip James Shaw
Thailand National Center for Genetic Engineering and Biotechnology
King Mongkut's University of Technology North Bangkok
Natioanl Electronic and Computer Technology Center
|Keywords:||Biochemistry, Genetics and Molecular Biology;Immunology and Microbiology|
|Citation:||BioMed Research International. Vol.2013, (2013)|
|Abstract:||Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time. © 2013 Anunchai Assawamakin et al.|
|Appears in Collections:||Scopus 2011-2015|
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