Education: Post-doctoral fellow, Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, 2004 Ph.D., Computer and Information Sciences, Temple University, Philadelphia, Pennsylvania, 2003 M.S., Electrical Engineering, University of Belgrade, Serbia, 1997 B.S., Electrical Engineering, University of Novi Sad, Serbia, 1994 Additional Positions: Director of the Ph.D. program in Computer Science, 2010- Recent Updates: (October 2010) Yong's paper accepted in Journal of Proteome Research. (July 2010) We announced AFP 2011 as a SIG meeting at ISMB 2011. The meeting features CAFA, the critical assessment of functional annotations. If you have a protein function prediction model, we hope you will participate! (July 2010) Pedja accepted to be director of the Computer Science Ph.D. program. Not first and not the last mistake I've made. (June 2010) Fuxiao's and Yong's structure-based kernels paper accepted in Bioinformatics. Fuxiao to present it at ISMB 2010 Student Council Symposium. (January 2009) Pedja's tutorial at Pac Symp Biocomput entitled "Molecular Bioinformatics for Diseases" can be downloaded here Research Interests: • Protein Bioinformatics Methods for characterization and prediction of protein's structural and functional properties, both on a whole-molecule and residue level. This includes automated inference of protein molecular and cellular function or disease associations from its sequence/structure/interactions, as well as understanding post-translational modifications, protein-partner binding sites, etc. We are also interested in understanding the molecular basis of disease via studying amino acid substitutions causing or associated with disease and biochemical ways they lead to altered phenotypes. See our algorithms and software for probabilistically identifying disease-associated human genes (PhenoPred) and biochemical basis of disease given a mutation (MutPred). • Computational Mass-Spectrometry Proteomics Methods for peptide identification, protein identification and protein quantification from tandem mass spectrometry (MS/MS) data. Each peptide in a mixture of digested proteins can be is associated with a probability to be detected by a mass spectrometry platform (that includes sample preparation, separation, mass spectrometer and software for peptide-to-spectrum matching). We hypothesized that this property, called peptide detectability, can be successfully inferred from amino acid sequence of a peptide and its parent protein. We use peptide detectability to build algorithms for protein inference and label-free quantification. See our algorithms and software for protein identification from MS/MS data (MSBayesPro). • Machine Learning and Data Mining Classification methods: prediction from biased, noisy, high-dimensional, class-imbalanced, and heterogeneous data. These methods include feature selection algorithms, estimation, exploiting unlabeled data, etc. See our work involving development of kernel methods for vertex labeling in sparse graphs (Graphlet Kernels), applied to the domain of protein function.