Predrag Radivojac

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Pedja Radivojac, Stanford University, 2013

Associate Professor of Computer Science and Informatics

Adjunct Associate Professor of Statistics


School of Informatics and Computing

Indiana University

150 South Woodlawn Avenue

Bloomington, IN 47405   

Phone:  (812) 856-1851

Fax:  (812) 856-1995

Office:  Lindley Hall 301F



Download my curriculum vitae in pdf format (last updated on 12/23/2013). Google Scholar profile. Academic genealogy.


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-

Associate Editor, PLoS Computational Biology, 2014-

Recent Updates:

  1. (April 2014) Pedja becomes Associate Editor for PLoS Computational Biology.

  2. (April 2014) Pedja gives talk at the Biocuration Society meeting, ISB 2014.

  3. (March 2014) Pedja to receive an R13 NIH grant to help support AFP 2014 special interest group meeting at ISMB 2014.

  4. (March 2014) Jose's paper proposing edit distance graphlet kernels accepted in Network Science. Congratulations!

  5. (March 2014) Our paper making a case for bioinformatics curriculum guidelines published in PLoS Computational Biology. The paper is available 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: April 12, 2014