Associate Professor of Computer Science and Informatics
Adjunct Associate Professor of Statistics
Address:
School of Informatics and Computing
150 South Woodlawn Avenue
Bloomington, IN 47405
Phone: (812) 856-1851
Fax: (812) 856-1995
Office: Lindley Hall 301F
Email:..predrag@indiana.edu..
Download my curriculum vitae in pdf format (last updated on 12/28/2012). Google Scholar profile.
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:
ISCB Board of Directors, 2012-
Editorial Board Member, Bioinformatics, Oxford University Press, 2010-
Recent Updates:
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(April
2013) Wyatt's paper "Information-theoretic evaluation of predicted
ontological annotations" accepted at
ISMB 2013. -
(April
2013) Pedja to give a highlights talk at
ISMB 2013.
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(March
2013) CAFA 2 to go in 2013-2014. Start anticipated for Summer 2013.
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(November 2012) Our paper "A large-scale evaluation of computational protein function prediction" by Radivojac et al. accepted in Nature Methods.
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(November 2012) Chao's paper "Extending the coverage of spectral libraries: a neighbor-based approach to predicting intensities of peptide fragmentation spectra" accepted in Proteomics.
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(November 2012) Kym and Jose get talks, Yuxiang poster presentation at Rocky 2012.
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.
Last modified: April 10, 2013