World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
16040
World Ranking
4002
National Ranking
1906

Research.com Recognitions

  • 2012 - Fellow of Alfred P. Sloan Foundation

Overview

Jinbo Xu is affiliated with the Toyota Technological Institute at Chicago in the United States. Their research primarily spans biochemistry, genetics, and molecular biology with a focus on molecular biology as the main subfield, alongside contributions to materials chemistry, genetics, ecology, and computer vision and pattern recognition.

Their scholarly output includes numerous recent publications covering topics such as protein structure and dynamics, machine learning in bioinformatics, enzyme structure and function, genomics and phylogenetic studies, RNA and protein synthesis mechanisms, vaccines and immunoinformatics approaches, and bioinformatics and genomic networks.

Frequent co-authors of Jinbo Xu include Ben Lai, Matt McPartlon, Xiaoyang Jing, Fandi Wu, and Xiao-Lei Wu, indicating collaboration within a focused research community.

Key publication venues where Jinbo Xu has contributed include bioRxiv (Cold Spring Harbor Laboratory), Zenodo (CERN European Organization for Nuclear Research), Bioinformatics, Briefings in Bioinformatics, and Proceedings of the National Academy of Sciences.

Notable recent papers involving Jinbo Xu are:

  • Improved protein structure prediction by deep learning irrespective of co-evolution information (2021, Nature Machine Intelligence)
  • Critical assessment of protein intrinsic disorder prediction (2021, Nature Methods)
  • The landscape of tolerated genetic variation in humans and primates (2023, Science)
  • Accurate protein function prediction via graph attention networks with predicted structure information (2021, Briefings in Bioinformatics)
  • Deep graph learning of inter-protein contacts (2021, Bioinformatics)

Jinbo Xu's research often integrates advanced computational techniques such as deep learning and graph networks applied to protein-related problems, supporting advances in structure prediction and function annotation.

In 2012, Jinbo Xu was recognized as a Fellow of the Alfred P. Sloan Foundation, an award acknowledging contributions to their field.

Best Publications

  • Opportunities and obstacles for deep learning in biology and medicine.

    Travers Ching;Daniel S. Himmelstein;Brett K. Beaulieu-Jones;Alexandr A. Kalinin

  • Template-based protein structure modeling using the RaptorX web server

    Morten Källberg;Morten Källberg;Haipeng Wang;Sheng Wang;Jian Peng

  • Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

    Sheng Wang;Siqi Sun;Zhen Li;Renyu Zhang

  • Global alignment of multiple protein interaction networks with application to functional orthology detection.

    Rohit Singh;Jinbo Xu;Bonnie Berger

  • Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

    Sheng Wang;Jian Peng;Jianzhu Ma;Jinbo Xu

  • RaptorX-Property: a web server for protein structure property prediction.

    Sheng Wang;Wei Li;Shiwang Liu;Jinbo Xu

  • Predicting the clinical impact of human mutation with deep neural networks

    Laksshman Sundaram;Laksshman Sundaram;Laksshman Sundaram;Hong Gao;Samskruthi Reddy Padigepati;Samskruthi Reddy Padigepati;Jeremy F. McRae

  • Raptorx: Exploiting structure information for protein alignment by statistical inference

    Jian Peng;Jinbo Xu

  • Distance-based protein folding powered by deep learning.

    Jinbo Xu

  • Pairwise global alignment of protein interaction networks by matching neighborhood topology

    Rohit Singh;Jinbo Xu;Bonnie Berger

  • Predicting protein-protein interactions through sequence-based deep learning.

    Somaye Hashemifar;Behnam Neyshabur;Aly A Khan;Jinbo Xu

  • RAPTOR: optimal protein threading by linear programming.

    Jinbo Xu;Ming Li;Dongsup Kim;Ying Xu

  • RaptorX server: a resource for template-based protein structure modeling.

    Morten Källberg;Gohar Margaryan;Sheng Wang;Jianzhu Ma

  • Improved protein structure prediction by deep learning irrespective of co-evolution information

    Jinbo Xu;Matthew McPartlon;Matthew McPartlon;Jin Li;Jin Li

  • Conditional Neural Fields

    Jian Peng;Liefeng Bo;Jinbo Xu

  • Protein structure alignment beyond spatial proximity

    Sheng Wang;Jianzhu Ma;Jian Peng;Jinbo Xu

  • Struct2Net: a web service to predict protein–protein interactions using a structure-based approach

    Rohit Singh;Daniel Kyu Park;Jinbo Xu;Raghavendra Hosur

  • Protein threading using context-specific alignment potential

    Jianzhu Ma;Sheng Wang;Feng Zhao;Jinbo Xu

  • Predicting protein contact map using evolutionary and physical constraints by integer programming

    Zhiyong Wang;Jinbo Xu

  • Analysis of distance-based protein structure prediction by deep learning in CASP13

    Jinbo Xu;Sheng Wang

  • Protein structure alignment beyond spatial proximity

    Sheng Wang;Jianzhu Ma;Jian Peng;Jinbo Xu

Frequent Co-Authors

Jian Peng
Jian Peng University of Illinois at Urbana-Champaign
Xin Gao
Xin Gao King Abdullah University of Science and Technology
Yizhou Yu
Yizhou Yu University of Hong Kong
Tobin R. Sosnick
Tobin R. Sosnick University of Chicago
Casey S. Greene
Casey S. Greene University of Colorado Denver
Anne E. Carpenter
Anne E. Carpenter Broad Institute
Inna Dubchak
Inna Dubchak Lawrence Berkeley National Laboratory
Ying Xu
Ying Xu University of Georgia
Anshul Kundaje
Anshul Kundaje Stanford University

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