scispace - formally typeset
D

Dongbo Bu

Researcher at Chinese Academy of Sciences

Publications -  103
Citations -  3162

Dongbo Bu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Protein structure prediction & Chemistry. The author has an hindex of 18, co-authored 90 publications receiving 2815 citations. Previous affiliations of Dongbo Bu include University of Waterloo.

Papers
More filters
Journal ArticleDOI

The Genomes of Oryza sativa: a history of duplications.

Jun Yu, +134 more
- 01 Feb 2005 - 
TL;DR: A more inclusive new approach for analyzing duplication history is introduced here, which reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications.
Journal ArticleDOI

Topological structure analysis of the protein–protein interaction network in budding yeast

TL;DR: A spectral method derived from graph theory was introduced to uncover hidden topological structures (i.e. quasi-cliques and quasi-bipartites) of complicated protein-protein interaction networks and suggest that they consist of biologically relevant functional groups.
Journal ArticleDOI

NONCODE: an integrated knowledge database of non-coding RNAs

TL;DR: A novel classification system, labeled process function class, to integrate existing classification systems and provides a user-friendly interface, a visualization platform and a convenient search option, allowing efficient recovery of sequence, regulatory elements in the flanking sequences, secondary structure, related publications and other information.
Journal ArticleDOI

Organization of the Caenorhabditis elegans small non-coding transcriptome: genomic features, biogenesis, and expression.

TL;DR: Two new classes of ncRNAs are identified, stem-bulge RNAs (sbRNAs) and snRNA-likeRNAs (snlRNAs), both featuring distinct internal motifs, secondary structures, upstream elements, and high and developmentally variable expression.
Journal ArticleDOI

Protein threading using residue co-variation and deep learning.

TL;DR: Experimental results suggest that predicted inter‐residue distance is helpful to both protein alignment and template selection especially for protein sequences without very close templates, and that the proposed DeepThreader method outperforms currently popular homology modeling method HHpred and threading method CNFpred by a large margin and greatly outperforms the latest contact‐assisted proteinthreading method EigenTHREADER.