J
Jinbo Xu
Researcher at Toyota Technological Institute at Chicago
Publications - 187
Citations - 12723
Jinbo Xu is an academic researcher from Toyota Technological Institute at Chicago. The author has contributed to research in topics: Protein structure prediction & Threading (protein sequence). The author has an hindex of 48, co-authored 174 publications receiving 10250 citations. Previous affiliations of Jinbo Xu include University of Waterloo & University of Chicago.
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Journal ArticleDOI
Template-based protein structure modeling using the RaptorX web server
Morten Källberg,Morten Källberg,Haipeng Wang,Sheng Wang,Jian Peng,Zhiyong Wang,Hui Lu,Jinbo Xu +7 more
TL;DR: This protocol presents a community-wide web-based method using RaptorX (http://raptorx.uchicago.edu/) for protein secondary structure prediction, template-based tertiary structure modeling, alignment quality assessment and sophisticated probabilistic alignment sampling.
Journal ArticleDOI
Opportunities and obstacles for deep learning in biology and medicine.
Travers Ching,Daniel Himmelstein,Brett K. Beaulieu-Jones,Alexandr A. Kalinin,Brian T. Do,Gregory P. Way,Enrico Ferrero,Paul-Michael Agapow,Michael Zietz,Michael M. Hoffman,Michael M. Hoffman,Wei Xie,Gail L. Rosen,Benjamin J. Lengerich,Johnny Israeli,Jack Lanchantin,Stephen Woloszynek,Anne E. Carpenter,Avanti Shrikumar,Jinbo Xu,Evan M. Cofer,Evan M. Cofer,Christopher A. Lavender,Srinivas C. Turaga,Amr Alexandari,Zhiyong Lu,David J. Harris,Dave DeCaprio,Yanjun Qi,Anshul Kundaje,Yifan Peng,Laura K. Wiley,Marwin H. S. Segler,Simina M. Boca,S. Joshua Swamidass,Austin Huang,Anthony Gitter,Anthony Gitter,Casey S. Greene +38 more
TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.
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Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.
TL;DR: A new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks that greatly outperforms existing methods and leads to much more accurate contact-assisted folding.
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Global alignment of multiple protein interaction networks with application to functional orthology detection.
TL;DR: This work introduces an algorithm, IsoRank, for global alignment of multiple PPI networks that simultaneously uses sequence similarity and network data and, unlike previous approaches, explicitly models the tradeoff inherent in combining them.
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RaptorX-Property: a web server for protein structure property prediction.
TL;DR: This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO) and it outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile.