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Computer Science

D-Index
56
Citations
23327
World Ranking
3959
National Ranking
1881

Biology and Biochemistry

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62
Citations
24502
World Ranking
10531
National Ranking
4569

Overview

Predrag Radivojac is affiliated with Northeastern University in the United States and conducts research primarily within the field of Biochemistry, Genetics, and Molecular Biology. Their work spans a diverse range of subfields including Molecular Biology, Genetics, Artificial Intelligence, Obstetrics and Gynecology, and Cancer Research.

The scientist's research addresses several main topics such as Genomics and Rare Diseases, Genomic Variations and Chromosomal Abnormalities, Bioinformatics and Genomic Networks, Genetic Associations and Epidemiology, Genomics and Phylogenetic Studies, Pregnancy and Preeclampsia Studies, and Cancer Genomics and Diagnostics.

Recent publications by Predrag Radivojac include the following papers:

  • Inferring the molecular and phenotypic impact of amino acid variants with MutPred2, 2020, Nature Communications
  • Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria, 2022, The American Journal of Human Genetics
  • Paternal age in rhesus macaques is positively associated with germline mutation accumulation but not with measures of offspring sociability, 2020, Genome Research
  • The ortholog conjecture revisited: the value of orthologs and paralogs in function prediction, 2020, Bioinformatics
  • Current and future directions in network biology, 2024, Bioinformatics Advances

Predrag Radivojac collaborates frequently with a number of researchers, including Steven E. Brenner, Vikas Pejaver, Constantina Bakolitsa, Anne O'Donnell-Luria, and Shantanu Jain.

Their publications appear often in venues such as bioRxiv (Cold Spring Harbor Laboratory), Human Genetics, arXiv (Cornell University), Bioinformatics, and Bioinformatics Advances.

Best Publications

  • REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants

    Nilah M M. Ioannidis;Joseph H H. Rothstein;Joseph H H. Rothstein;Vikas Pejaver;Sumit Middha

  • The importance of intrinsic disorder for protein phosphorylation

    Lilia M. Iakoucheva;Predrag Radivojac;Celeste J. Brown;Celeste J. Brown;Timothy R. O’Connor

  • A large-scale evaluation of computational protein function prediction

    Predrag Radivojac;Wyatt T Clark;Tal Ronnen Oron;Alexandra M Schnoes

  • Length-dependent prediction of protein intrinsic disorder

    Kang Peng;Predrag Radivojac;Slobodan Vucetic;A Keith Dunker

  • Automated inference of molecular mechanisms of disease from amino acid substitutions

    Biao Li;Vidhya G. Krishnan;Matthew E. Mort;Fuxiao Xin

  • Analysis of molecular recognition features (MoRFs).

    Amrita Mohan;Christopher J. Oldfield;Predrag Radivojac;Vladimir Vacic

  • Intrinsic Disorder and Functional Proteomics

    Predrag Radivojac;Lilia M. Iakoucheva;Christopher J. Oldfield;Zoran Obradovic

  • Inferring the molecular and phenotypic impact of amino acid variants with MutPred2.

    Vikas Pejaver;Vikas Pejaver;Jorge Urresti;Jose Lugo-Martinez;Jose Lugo-Martinez;Kymberleigh A. Pagel;Kymberleigh A. Pagel

  • Identification, Analysis and Prediction of Protein Ubiquitination Sites

    Predrag Radivojac;Vladimir Vacic;Chad Haynes;Ross R. Cocklin

  • Intrinsic disorder is a common feature of hub proteins from four eukaryotic interactomes.

    Chad Haynes;Christopher J Oldfield;Fei Ji;Niels Klitgord

  • Exploiting heterogeneous sequence properties improves prediction of protein disorder.

    Zoran Obradovic;Kang Peng;Slobodan Vucetic;Predrag Radivojac

  • Two Sample Logo: a graphical representation of the differences between two sets of sequence alignments

    Vladimir Vacic;Lilia M. Iakoucheva;Predrag Radivojac

  • Optimizing long intrinsic disorder predictors with protein evolutionary information.

    Kang Peng;Slobodan Vucetic;Predrag Radivojac;Celeste J. Brown

  • Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria

    Unknown

  • Predicting Intrinsic Disorder From Amino Acid Sequence

    Zoran Obradovic;Kang Peng;Slobodan Vucetic;Predrag Radivojac

  • Characterization of Molecular Recognition Features, MoRFs, and Their Binding Partners

    Vladimir Vacic;Christopher J. Oldfield;Amrita Mohan;Predrag Radivojac

  • Alternative splicing in concert with protein intrinsic disorder enables increased functional diversity in multicellular organisms

    Pedro R. Romero;Saima Zaidi;Ya Yin Fang;Vladimir N. Uversky

  • An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T. Clark;Asma R. Bankapur

  • The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

    Naihui Zhou;Yuxiang Jiang;Timothy R. Bergquist;Alexandra J. Lee

  • Protein flexibility and intrinsic disorder.

    Predrag Radivojac;Zoran Obradovic;David K. Smith;Guang Zhu

  • The structural and functional signatures of proteins that undergo multiple events of post-translational modification.

    Vikas Pejaver;Wei Lun Hsu;Fuxiao Xin;Fuxiao Xin;A. Keith Dunker

Frequent Co-Authors

Sean D. Mooney
Sean D. Mooney University of Washington
A. Keith Dunker
A. Keith Dunker Indiana University
Vladimir N. Uversky
Vladimir N. Uversky University of South Florida
Haixu Tang
Haixu Tang Indiana University
Zoran Obradovic
Zoran Obradovic Temple University
Matthew W. Hahn
Matthew W. Hahn Indiana University
Slobodan Vucetic
Slobodan Vucetic Temple University
Steven E. Brenner
Steven E. Brenner University of California, Berkeley
David Neil Cooper
David Neil Cooper Cardiff University
Matthew Mort
Matthew Mort Cardiff University

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