Biomarker Selection and Classification of “-Omics” Data Using a Two-Step Bayes Classification Framework

• Published in: BioMed research international Vol. 2013; no. 2013; pp. 1 - 9
• Main Authors: Tongsima, Sissades, Varavithya, Vara, Shaw, Philip James, Kulawonganunchai, Supasak, Prueksaaroon, Supakit, Assawamakin, Anunchai, Ruangrajitpakorn, Taneth
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2013; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial Intelligence; Bayes Theorem; Bayesian analysis; Biological markers; Biomarkers; Gene Expression Profiling; Humans; Microarray Analysis - statistics & numerical data; Models, Statistical; Oligonucleotide Array Sequence Analysis; Phenotype; Physiological aspects; Polymorphism, Single Nucleotide; Proteomics - methods; Proteomics - statistics & numerical data; Thailand
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2013/148014

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.