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| 1 | Topological structure analysis of the protein-protein interaction network in budding yeast显示文摘 | Bu Dongbo Zhao Yi Cai Lun | 2003 | Nucleic Acids Res2003，31，9： | 1 |
| 2 | Biological data processing based on bio-processor unit(BPU), a new concept for next generation computational biology显示文摘The revolution of biotechnology has pushed forward life sciences into the Big Data era.Particularly,high-throughput bio-techniques have greatly accelerated the integration of biology,computing and informatics,and hence substantially pushed forward the development of bioinformatics and computational biology.According to the latest report of data deposition within National Centre for Biotechnology Information(NCBI),the genome sequencing projects have increased 49.94%,the sequence reads generated from next generation sequencing have increased 44.37%and the protein sequences have increased 39.85%,compared with | Di Liu Dongbo Bu Tieliu Shi Jianxiao Quan Depeng Wang Yongyong Shi Xiao-Chen Bo Wenbao Han | 2018 | Science China(Life Sciences)2018，61，5： | 0 |
| 3 | Constrained maximum weighted bipartite matching:a novel approach to radio broadcast scheduling显示文摘Given a set of radio broadcast programs, the radio broadcast scheduling problem is to allocate a set of devices to transmit the programs to achieve the optimal sound quality. In this article, we propose a complete algorithm to solve the problem, which is based on a branch-and-bound(BnB) algorithm. We formulate the problem with a new model, called constrained maximum weighted bipartite matching(CMBM),i.e., the maximum matching problem on a weighted bipartite graph with constraints. For the reduced matching problem, we propose a novel BnB algorithm by introducing three new strategies, including the highest quality first, the least conflict first and the more edge first. We also establish an upper bound estimating function for pruning the search space of the algorithm. The experimental results show that our new algorithm can quickly find the optimal solution for the radio broadcast scheduling problem at small scales, and has higher scalability for the problems at large scales than the existing complete algorithm. | Shaojiang WANG Tianyong WU Yuan YAO Dongbo BU Shaowei CAI | 2019 | Science China(Information Sciences)2019，62，7： | 0 |
| 4 | Phylogeny of SARS-CoV as inferred from complete genome comparison显示文摘SARS-CoV, as the pathogeny of severe acute respiratory syndrome (SARS), is a mystery that the origin of the virus is still unknown even a few isolates of the virus were completely sequenced. To explore the genesis of SARS-CoV, the FDOD method previously developed by us was applied to comparing complete genomes from 12 SARS-CoV isolates to those from 12 previously identified coronaviruses and an unrooted phylogenetic tree was constructed. Our results show that all SARS-CoV isolates were clustered into a clique and previously identified coronaviruses formed the other clique. Meanwhile, the three groups of coronaviruses depart from each other clearly in our tree that is consistent with the results of prevenient papers. Differently, from the topology of the phylogenetic tree we found that SARS-CoV is more close to group 1 within genus coronavirus. The topology map also shows that the 12 SARS-CoV isolates may be divided into two groups determined by the association with the SARS-CoV from the Hotel M in Hong Kong that may give some information about the infectious relationship of the SARS. | QI Zhen HU Yu LI Wei CHEN Yanjun ZHANG Zhihua SUN Shiwei LU Hongchao ZHANG Jingfen BU Dongbo LING Lunjiang CHEN Runsheng | 2003 | Chinese Science Bulletin2003，48，12： | 0 |
| 5 | Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms显示文摘Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer science.These researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure.These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic modeling.Recently,we have also witnessed the great success of deep learning in protein structure prediction.In this review,we present a survey of the efforts for protein structure prediction.We compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep learning.In short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired. | Bin Huang Lupeng Kong Chao Wang Fusong Ju Qi Zhang Jianwei Zhu Tiansu Gong Haicang Zhang Chungong Yu Wei-Mou Zheng Dongbo Bu | 2023 | Genomics, Proteomics & Bioinformatics2023，21，5： | 0 |
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