Predrag Radivojac

[Teaching] [Research] [People] [Publications] [Software] [FAQ]


Pedja Radivojac, Stanford University, 2013

Associate Professor of Computer Science and Informatics

Adjunct Associate Professor of Statistics


Department of Computer Science and Informatics

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 12/21/2014). Google Scholar profile.


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

Professional Activities:

Board of Directors Member, International Society for Computational Biology (ISCB), 2012-

Associate Editor, PLoS Computational Biology, 2014-

Editorial Board Member, Bioinformatics, Oxford University Press, 2010-

Recent Updates:

  1. (February 2015) Zhiyu to join Novilytic as a post-doctoral fellow.

  2. (February 2015) Our 10 Simple Rules paper accepted to PLoS Computational Biology.

  3. (January 2015) Pedja's second ISCB Board of Directors term started (ends in January 2018).

  4. (December 2014) Our PSB 2015 workshop abstract available online.

  5. (September 2014) Jose's paper on edit distance graphlet kernels officially published in Network Science. Available here.

  6. (August 2014) I will be a conference chair for GLBIO 2015 at the Purdue University campus.  

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: February 07, 2015