Anunchai AssawamakinSupakit PrueksaaroonSupasak KulawonganunchaiPhilip James ShawVara VaravithyaTaneth RuangrajitpakornSissades TongsimaMahidol UniversityThammasat UniversityThailand National Center for Genetic Engineering and BiotechnologyKing Mongkut's University of Technology North BangkokNatioanl Electronic and Computer Technology Center2018-10-192018-10-192013-10-07BioMed Research International. Vol.2013, (2013)23146141231461332-s2.0-84884861449https://repository.li.mahidol.ac.th/handle/123456789/31192Identification 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.Mahidol UniversityBiochemistry, Genetics and Molecular BiologyImmunology and MicrobiologyArticleSCOPUS10.1155/2013/148014