Assistant Professor of Informatics and Computing
Adjunct Assistant Professor of Statistics
Address:
School of Informatics and Computing
901 East 10th Street
Bloomington, IN 47408
Phone: (812) 856-1851
Fax: (812) 856-1995
Office: Informatics West 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 12/16/2009).
Recent Updates:
-
(November 2009) Biao's and Fuxiao's paper "Automated inference of molecular mechanisms
of disease from amino acid substitutions" published in
Bioinformatics
-
(November 2009)
Amrita had a successful dissertation defense on 10/23. Dr. Mohan is now a
post-doc in cancer systems biology at OSI Pharmaceuticals.
-
(October 2009)
Shuyan receives a travel award from the National Library of Medicine (NIH)
for PSB
2010.
-
(September 2009)
Shuyan's paper "Loss of post-translational modification sites in disease"
accepted to
Pac Symp Biocomput
-
(September 2009) Amrita's
paper "Influence of sequence changes and environment on intrinsically
disordered proteins" published in
PLoS Comput Biol
-
(August 2009) Yong's paper "A Bayesian approach to protein inference problem in shotgun proteomics" published in J Comput Biol
-
(July 2009) Pedja's and Amrita's paper "Identification, analysis and prediction of protein ubiquitination sites" accepted to Proteins
-
(May 2009) Pedja's paper "Graphlet kernels for prediction of functional residues in protein structures" accepted to J Comput Biol
-
(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.
Last modified: January 17, 2010