基本信息
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职业迁徙
个人简介
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:
Director of the Ph.D. program in Computer Science, 2010-
Recent Updates:
(October 2010) Yong's paper accepted in Journal of Proteome Research.
(July 2010) We announced AFP 2011 as a SIG meeting at ISMB 2011. The meeting features CAFA, the critical assessment of functional annotations. If you have a protein function prediction model, we hope you will participate!
(July 2010) Pedja accepted to be director of the Computer Science Ph.D. program. Not first and not the last mistake I've made.
(June 2010) Fuxiao's and Yong's structure-based kernels paper accepted in Bioinformatics. Fuxiao to present it at ISMB 2010 Student Council Symposium.
(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.
研究兴趣
论文共 226 篇作者统计合作学者相似作者
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Damiano Piovesan, Davide Zago,Parnal Joshi,M Clara De Paolis Kaluza, Mahta Mehdiabadi, Rashika Ramola,Alexander Miguel Monzon, Walter Reade,Iddo Friedberg,Predrag Radivojac,Silvio C E Tosatto
Bioinformatics advancesno. 1 (2024): vbae043-vbae043
medRxiv : the preprint server for health sciences (2024)
Sarah L Stenton,Vikas Pejaver,Timothy Bergquist,Leslie G Biesecker,Alicia B Byrne, Emily Nadeau,Marc S Greenblatt,Steven Harrison,Sean Tavtigian,Predrag Radivojac,Steven E Brenner, Anne O'Donnell-Luria
medRxiv : the preprint server for health sciences (2024)
Katherine R Chao, Lily Wang, Ruchit Panchal, Calwing Liao, Haneen Abderrazzaq, Robert Ye, Patrick Schultz, John Compitello, Riley H Grant, Jack A Kosmicki, Ben Weisburd, William Phu,
bioRxiv : the preprint server for biology (2024)
HAL (Le Centre pour la Communication Scientifique Directe) (2023)
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medRxiv (Cold Spring Harbor Laboratory) (2023)
Frontiers in artificial intelligence (2023): 1029943
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