Selective reaction monitoring (SRM) has become one of the main methods for low-mass range targeted proteomics by mass spectrometry (MS). However, in most SRM-MS biomarker validation studies the sample size is very small, and in particular smaller than the number of proteins measured in the experiment. Moreover, the data can be noisy due to a low number of ions detected per peptide by the instrument. In this paper, those issues are addressed by a model-based Bayesian method for classification of SRM-MS data, which relies on the SRM model proposed by Esmaeil and collaborators and builds a kernel classifier, similarly to the classifier for LC-MS data proposed by Banerjee and Braga-Neto. The methodology is likelihood-free, using Approximate Bayesian Computation (ABC) implemented via a Markov Chain Monte Carlo (MCMC) procedure and a kernel-based Optimal Bayesian Classifier (OBC). Extensive experimental results demonstrate that the proposed method is superior to classical methods, such as LDA and 3NN, when sample size is small, dimensionality is large, the data are noisy, or a combination of these. *Reprinted with permission from Bayesian Classification of Proteomics Biomarkers from Selected Reaction Monitoring Data using an Approximate Bayesian Computation-Markov Chain Monte Carlo Approach by Kashyap Nagaraja and Ulisses Braga-Neto from Cancer Informatics journal Volume 17, pages 1:7, DOI:10.1177/1176935118786927, Copyright Date: May 24, 2018, Owners: Kashyap Nagaraja and Ulisses BragaNeto.