Assistant Professor
Address:
901 East 10th Street
Bloomington, IN 47408
Phone: (812) 856-1851
Fax: (812) 856-1995
Office: Informatics 219
Email:..predrag@indiana.edu..
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
Download my curriculum vitae in pdf format (last updated on 06/06/2009).
Recent updates:
• AMIA Summit on Translational Bioinformatics 2009
Summit on Translational Bioinformatics (STB 2009) is a meeting dedicated to bioinformatics research and applications in the fields encompassing medical and clinical domains. This symposium was held March 15-17, 2009 and was sponsored by AMIA and ISCB.
• PSB 2009
"Molecular Bioinformatics for Diseases" is a session I was co-chairing at the 14th Pacific Symposium on Biocomputing, PSB 2009. This session was organized with Atul Butte (Stanford U.), Maricel Kann (UMBC), Yves Lussier (U. Chicago), and Yanay Ofran (Bar-Ilan U.). This was the fourth and last part of the series we organized from 2006 till 2009. Tutorial from the session can be downloaded here.
Research Interests:
• Protein Bioinformatics
Methods for characterization and prediction of protein's structural and functional properties, both on a whole-sequence and residue level. This includes automated inference of GO protein annotation 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 phenotype. See our algorithms and software for probabilistically identifying disease-associated human genes (PhenoPred). We can also predict whether an amino acid substitution is disease-associated and if so, what is the most likely biochemical basis of disease.
• 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 viewed through its probability to be detected by a mass spectrometry platform (that includes sample processing, separation process, mass spectrometer and software for spectrum-peptide 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 tandem MS data (MSBayesPro).
• Machine Learning/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 node classification in sparse labeled graphs (Graphlet Kernels), applied to the domain of protein function.
Last modified: June 06, 2009