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Title: Biomarker selection and classification of "- Omics " data using a two-step bayes classification framework
Authors: Anunchai Assawamakin
Supakit Prueksaaroon
Supasak Kulawonganunchai
Philip James Shaw
Vara Varavithya
Taneth Ruangrajitpakorn
Sissades Tongsima
Mahidol University
Thammasat University
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
Issue Date: 7-Oct-2013
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.
ISSN: 23146141
Appears in Collections:Scopus 2011-2015

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